{"cells":[{"metadata":{"id":"E1FFE94721D74ECE804CFD17CAB592F2","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# 环境"},{"metadata":{"id":"0485451F30A444E78BC00CB426A8776A","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"Collecting pip\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4e/5f/528232275f6509b1fff703c9280e58951a81abe24640905de621c9f81839/pip-20.2.3-py2.py3-none-any.whl (1.5MB)\n\u001b[K    100% |████████████████████████████████| 1.5MB 844kB/s eta 0:00:01\n\u001b[?25hInstalling collected packages: pip\n  Found existing installation: pip 9.0.1\n    Uninstalling pip-9.0.1:\n      Successfully uninstalled pip-9.0.1\nSuccessfully installed pip-20.2.3\n","name":"stdout"}],"source":"!pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple","execution_count":1},{"metadata":{"id":"52BADC203A5F426E9907B9D59A06B4E3","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":true},"cell_type":"code","outputs":[{"output_type":"stream","text":"Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting tensorflow==2.3.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/97/ae/0b08f53498417914f2274cc3b5576d2b83179b0cbb209457d0fde0152174/tensorflow-2.3.0-cp36-cp36m-manylinux2010_x86_64.whl (320.4 MB)\n\u001b[K     |████████████████████████████████| 320.4 MB 26 kB/s  eta 0:00:013            | 3.3 MB 4.8 MB/s eta 0:01:07██▉                         | 68.4 MB 515 kB/s eta 0:08:09███▊                 | 147.3 MB 8.0 MB/s eta 0:00:22    |███████████████▊                | 157.2 MB 3.5 MB/s eta 0:00:48��███████████████████▌   | 285.7 MB 4.5 MB/s eta 0:00:08��████████████████████  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6.8 MB 31 kB/s  eta 0:00:01\n\u001b[?25hCollecting tensorflow-estimator<2.4.0,>=2.3.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e9/ed/5853ec0ae380cba4588eab1524e18ece1583b65f7ae0e97321f5ff9dfd60/tensorflow_estimator-2.3.0-py2.py3-none-any.whl (459 kB)\n\u001b[K     |████████████████████████████████| 459 kB 6.0 MB/s eta 0:00:01\n\u001b[?25hCollecting opt-einsum>=2.3.2\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n\u001b[K     |████████████████████████████████| 65 kB 4.5 MB/s eta 0:00:011\n\u001b[?25hCollecting h5py<2.11.0,>=2.10.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/60/06/cafdd44889200e5438b897388f3075b52a8ef01f28a17366d91de0fa2d05/h5py-2.10.0-cp36-cp36m-manylinux1_x86_64.whl (2.9 MB)\n\u001b[K     |████████████████████████████████| 2.9 MB 6.2 MB/s eta 0:00:01:01\n\u001b[?25hCollecting protobuf>=3.9.2\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/30/79/510974552cebff2ba04038544799450defe75e96ea5f1675dbf72cc8744f/protobuf-3.13.0-cp36-cp36m-manylinux1_x86_64.whl (1.3 MB)\n\u001b[K     |████████████████████████████████| 1.3 MB 2.4 MB/s eta 0:00:01\n\u001b[?25hCollecting google-pasta>=0.1.8\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57 kB)\n\u001b[K     |████████████████████████████████| 57 kB 7.2 MB/s  eta 0:00:01\n\u001b[?25hCollecting gast==0.3.3\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d6/84/759f5dd23fec8ba71952d97bcc7e2c9d7d63bdc582421f3cd4be845f0c98/gast-0.3.3-py2.py3-none-any.whl (9.7 kB)\nRequirement already satisfied: absl-py>=0.7.0 in /opt/conda/lib/python3.6/site-packages (from tensorflow==2.3.0) (0.7.1)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.6/site-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.0) (3.1.1)\nRequirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.6/site-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.0) (0.15.4)\nCollecting google-auth-oauthlib<0.5,>=0.4.1\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7b/b8/88def36e74bee9fce511c9519571f4e485e890093ab7442284f4ffaef60b/google_auth_oauthlib-0.4.1-py2.py3-none-any.whl (18 kB)\nCollecting tensorboard-plugin-wit>=1.6.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b6/85/5c5ac0a8c5efdfab916e9c6bc18963f6a6996a8a1e19ec4ad8c9ac9c623c/tensorboard_plugin_wit-1.7.0-py3-none-any.whl (779 kB)\n\u001b[K     |████████████████████████████████| 779 kB 10.7 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.6/site-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.0) (49.2.0)\nCollecting requests<3,>=2.21.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/45/1e/0c169c6a5381e241ba7404532c16a21d86ab872c9bed8bdcd4c423954103/requests-2.24.0-py2.py3-none-any.whl (61 kB)\n\u001b[K     |████████████████████████████████| 61 kB 849 kB/s  eta 0:00:01\n\u001b[?25hCollecting google-auth<2,>=1.6.3\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1f/cf/724b6436967a8be879c8de16b09fd80e0e7b0bcad462f5c09ee021605785/google_auth-1.22.1-py2.py3-none-any.whl (114 kB)\n\u001b[K     |████████████████████████████████| 114 kB 14.2 MB/s eta 0:00:01\n\u001b[?25hCollecting requests-oauthlib>=0.7.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/12/b92740d845ab62ea4edf04d2f4164d82532b5a0b03836d4d4e71c6f3d379/requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (2018.11.29)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (1.22)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (2.6)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (3.0.4)\nRequirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.5\" in /opt/conda/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (4.5)\nCollecting cachetools<5.0,>=2.0.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cd/5c/f3aa86b6d5482f3051b433c7616668a9b96fbe49a622210e2c9781938a5c/cachetools-4.1.1-py3-none-any.whl (10 kB)\nCollecting pyasn1-modules>=0.2.1\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/de/214830a981892a3e286c3794f41ae67a4495df1108c3da8a9f62159b9a9d/pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)\n\u001b[K     |████████████████████████████████| 155 kB 14.2 MB/s eta 0:00:01\n\u001b[?25hCollecting oauthlib>=3.0.0\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/05/57/ce2e7a8fa7c0afb54a0581b14a65b56e62b5759dbc98e80627142b8a3704/oauthlib-3.1.0-py2.py3-none-any.whl (147 kB)\n\u001b[K     |████████████████████████████████| 147 kB 14.1 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: pyasn1>=0.1.3 in /opt/conda/lib/python3.6/site-packages (from rsa<5,>=3.1.4; python_version >= \"3.5\"->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3.0) (0.4.8)\nInstalling collected packages: astunparse, scipy, keras-preprocessing, protobuf, cachetools, pyasn1-modules, google-auth, oauthlib, requests, requests-oauthlib, google-auth-oauthlib, tensorboard-plugin-wit, tensorboard, tensorflow-estimator, opt-einsum, h5py, google-pasta, gast, tensorflow\n  Attempting uninstall: scipy\n    Found existing installation: scipy 1.2.0\n    Uninstalling scipy-1.2.0:\n      Successfully uninstalled scipy-1.2.0\n  Attempting uninstall: keras-preprocessing\n    Found existing installation: Keras-Preprocessing 1.1.0\n    Uninstalling Keras-Preprocessing-1.1.0:\n      Successfully uninstalled Keras-Preprocessing-1.1.0\n  Attempting uninstall: protobuf\n    Found existing installation: protobuf 3.8.0\n    Uninstalling protobuf-3.8.0:\n      Successfully uninstalled protobuf-3.8.0\n  Attempting uninstall: requests\n    Found existing installation: requests 2.18.4\n    Uninstalling requests-2.18.4:\n      Successfully uninstalled requests-2.18.4\n  Attempting uninstall: tensorboard\n    Found existing installation: tensorboard 1.13.1\n    Uninstalling tensorboard-1.13.1:\n      Successfully uninstalled tensorboard-1.13.1\n  Attempting uninstall: tensorflow-estimator\n    Found existing installation: tensorflow-estimator 1.13.0\n    Uninstalling tensorflow-estimator-1.13.0:\n      Successfully uninstalled tensorflow-estimator-1.13.0\n  Attempting uninstall: h5py\n    Found existing installation: h5py 2.8.0rc1\n    Uninstalling h5py-2.8.0rc1:\n      Successfully uninstalled h5py-2.8.0rc1\n  Attempting uninstall: gast\n    Found existing installation: gast 0.2.2\n    Uninstalling gast-0.2.2:\n      Successfully uninstalled gast-0.2.2\n  Attempting uninstall: tensorflow\n    Found existing installation: tensorflow 1.13.1\n    Uninstalling tensorflow-1.13.1:\n      Successfully uninstalled tensorflow-1.13.1\n\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n\nWe recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n\ntensorboard 2.3.0 requires grpcio>=1.24.3, but you'll have grpcio 1.22.0 which is incompatible.\nspacy 2.1.4 requires jsonschema<3.1.0,>=2.6.0, but you'll have jsonschema 3.2.0 which is incompatible.\npaddlepaddle 1.5.0 requires matplotlib<=2.2.4, but you'll have matplotlib 3.1.1 which is incompatible.\npaddlepaddle 1.5.0 requires nltk<=3.4,>=3.2.2, but you'll have nltk 3.4.1 which is incompatible.\npaddlepaddle 1.5.0 requires scipy<=1.2.1,>=0.19.0, but you'll have scipy 1.4.1 which is incompatible.\nmxnet 1.4.1 requires numpy<1.15.0,>=1.8.2, but you'll have numpy 1.16.3 which is incompatible.\nmoto 1.3.9 requires Jinja2>=2.10.1, but you'll have jinja2 2.10 which is incompatible.\nmoto 1.3.9 requires PyYAML==3.13, but you'll have pyyaml 5.3.1 which is incompatible.\njupyter-kernel-gateway 1.2.0 requires jupyter-client<5.0,>=4.2.0, but you'll have jupyter-client 6.1.6 which is incompatible.\nauto-sklearn 0.5.2 requires scikit-learn<0.20,>=0.19, but you'll have scikit-learn 0.21.1 which is incompatible.\u001b[0m\nSuccessfully installed astunparse-1.6.3 cachetools-4.1.1 gast-0.3.3 google-auth-1.22.1 google-auth-oauthlib-0.4.1 google-pasta-0.2.0 h5py-2.10.0 keras-preprocessing-1.1.2 oauthlib-3.1.0 opt-einsum-3.3.0 protobuf-3.13.0 pyasn1-modules-0.2.8 requests-2.24.0 requests-oauthlib-1.3.0 scipy-1.4.1 tensorboard-2.3.0 tensorboard-plugin-wit-1.7.0 tensorflow-2.3.0 tensorflow-estimator-2.3.0\n","name":"stdout"}],"source":"!pip install tensorflow==2.3.0 -i https://pypi.tuna.tsinghua.edu.cn/simple","execution_count":2},{"metadata":{"id":"05FE64A9152B4109810CF08F2DAF3EFE","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting deepctr\n  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5d/61/fb1c7f06f0fed2be82068f365824532afcf0bbed77e85cdb4107196ea0bf/deepctr-0.8.2-py3-none-any.whl (110 kB)\n\u001b[K     |████████████████████████████████| 110 kB 1.6 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: h5py in /opt/conda/lib/python3.6/site-packages (from deepctr) (2.10.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from deepctr) (2.24.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from h5py->deepctr) (1.15.0)\nRequirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.6/site-packages (from h5py->deepctr) (1.16.3)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests->deepctr) (1.22)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests->deepctr) (2.6)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests->deepctr) (3.0.4)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests->deepctr) (2018.11.29)\nInstalling collected packages: deepctr\nSuccessfully installed deepctr-0.8.2\n","name":"stdout"}],"source":"!pip install deepctr -i https://pypi.tuna.tsinghua.edu.cn/simple","execution_count":3},{"metadata":{"id":"6E9138EA7B9B46E38A02AE07F46E4394","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\r\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.6/site-packages (4.49.0)\r\n","name":"stdout"}],"source":"!pip install tqdm -i https://pypi.tuna.tsinghua.edu.cn/simple","execution_count":4},{"metadata":{"id":"0CEBBFBFCDED4327999CB2201C6B71B1","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# 提交"},{"metadata":{"id":"89E923EE37EA4B3C8A5D02C5B2C31757","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"!wget -nv -O kesci_submit https://cdn.kesci.com/submit_tool/v4/kesci_submit&&chmod +x kesci_submit","execution_count":5},{"metadata":{"id":"8D61E07BB1304576A5DA6343E1BF95C3","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"!./kesci_submit -token d8082488eee28119 -file /home/kesci/work/sub.csv","execution_count":6},{"metadata":{"id":"A5DBA0FA322D4F23A8AE83800EEF0759","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# 数据预处理&EDA"},{"metadata":{"id":"915A060EB45648F98370C94BA2C6799D","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## train"},{"metadata":{"id":"6A301F8046D5462C87299F1012ED6688","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:34: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n","name":"stderr"}],"source":"import pandas as pd\nimport gc\ntrain = pd.read_csv('/home/kesci/data/competition_A/train_set.csv').replace(' ', -1).fillna(-1)\n# print(train.columns)\n# print(train.isnull().values.any())\n# print(set(train['Gender\\n性别'].values.tolist()))\ntrain_group = train.groupby('护理来源')\ndel train\ngc.collect()\n\n# from sklearn.preprocessing import OneHotEncoder\n# enc = OneHotEncoder(sparse = False)\n# result = enc.fit_transform(train[['Source of Care\\n护理来源']])   \n# enc.fit([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]])\n# result = enc.transform(data[[41]])\n\ndic_1 = {'Private Hospital':1, -1:2, 'Governament Hospital':3, 'Never Counsulted':4, 'clinic':5}\nbuf_1 = pd.DataFrame()\nfor name,group in train_group:\n    group['护理来源'] = dic_1[name]\n    buf_1 = pd.concat([buf_1,group],ignore_index=True)\n\ndic_2 = {'F':1, 'M':2}\nbuf_1_group = buf_1.groupby('性别')\nbuf_2 = pd.DataFrame()\nfor name,group in buf_1_group:\n    group['性别'] = dic_2[name]\n    buf_2 = pd.concat([buf_2,group],ignore_index=True)\n    \ndic_3 = {'north':1, 'east':2, 'south':3, 'west':4}    \nbuf_3 = pd.DataFrame()\nbuf_2_group = buf_2.groupby('区域')\nfor name,group in buf_2_group:\n    group['区域'] = dic_3[name]\n    buf_3 = pd.concat([buf_3,group],ignore_index=True)\n\nbuf_3 = buf_3.astype(float)\n\ncat_list = ['肥胖腰围',\n            '教育', '未婚',\n            '护理来源', '视力不佳',\n            '饮酒', '高血压',\n            '家庭高血压', '糖尿病',\n            '家族糖尿病', '肝炎', '家族肝炎',\n            '慢性疲劳']\nfor i in cat_list:\n    buf_3[i] = buf_3[i].astype(int)\n\nbuf_3.to_csv('train_.csv',index=0)\n","execution_count":5},{"metadata":{"id":"031E8FBF0E4543628475A45AA9FF38AF","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## test"},{"metadata":{"id":"F35EFCDF83E34415BC3BDF76DD506955","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:34: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n","name":"stderr"}],"source":"import pandas as pd\nimport gc\ntest = pd.read_csv('/home/kesci/data/competition_A/test_set.csv').replace(' ', -1).fillna(-1)\n# print(train.columns)\n# print(train.isnull().values.any())\n# print(set(train['Gender\\n性别'].values.tolist()))\ntest_group = test.groupby('护理来源')\ndel test\ngc.collect()\n\n# from sklearn.preprocessing import OneHotEncoder\n# enc = OneHotEncoder(sparse = False)\n# result = enc.fit_transform(train[['Source of Care\\n护理来源']])   \n# enc.fit([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]])\n# result = enc.transform(data[[41]])\n\ndic_1 = {'Private Hospital':1, -1:2, 'Governament Hospital':3, 'Never Counsulted':4, 'clinic':5}\nbuf_1 = pd.DataFrame()\nfor name,group in test_group:\n    group['护理来源'] = dic_1[name]\n    buf_1 = pd.concat([buf_1,group],ignore_index=True)\n\ndic_2 = {'F':1, 'M':2}\nbuf_1_group = buf_1.groupby('性别')\nbuf_2 = pd.DataFrame()\nfor name,group in buf_1_group:\n    group['性别'] = dic_2[name]\n    buf_2 = pd.concat([buf_2,group],ignore_index=True)\n    \ndic_3 = {'north':1, 'east':2, 'south':3, 'west':4}    \nbuf_3 = pd.DataFrame()\nbuf_2_group = buf_2.groupby('区域')\nfor name,group in buf_2_group:\n    group['区域'] = dic_3[name]\n    buf_3 = pd.concat([buf_3,group],ignore_index=True)\n\nbuf_3 = buf_3.astype(float)\n\ncat_list = ['肥胖腰围',\n            '教育', '未婚',\n            '护理来源', '视力不佳',\n            '饮酒', '高血压',\n            '家庭高血压', '糖尿病',\n            '家族糖尿病', '家族肝炎',\n            '慢性疲劳']\nfor i in cat_list:\n    buf_3[i] = buf_3[i].astype(int)\n\nbuf_3.to_csv('test_.csv',index=0)\n","execution_count":6},{"metadata":{"id":"7FFF8BB6A02A4C24929C77C259A02F37","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## target enc"},{"metadata":{"id":"20FCC0327D5F4607BE54790B51AB0347","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"from tqdm import tqdm\nfrom sklearn.model_selection import StratifiedKFold\n\ndata_test = pd.read_csv('test_.csv')\n# test = data_test.drop(columns=['ID'])\n\ndata_train = pd.read_csv('train_.csv').sort_values(by=['肝炎'])#.tail(5000)\ndata_train = data_train[ ~ data_train['肝炎'].isin([-1])] \n# train = data_train.drop(columns=['ID','肝炎'])\ntrain_ = data_train.drop(columns=['肝炎'])\n\nlabel = data_train['肝炎']\n\ndata = train_.append(data_test)\n\ndata.columns = ['Age','Gender','Area','Weight','Height','Body_mass_index',\n                'Obesity_waistline','Waist','Highest_blood_pressure','Minimum_blood_pressure',\n                'Good_Cholesterol','Bad_Cholesterol','Total_Cholesterol','Blood_lipid_abnormality',\n                'PVD','Sports_activities','Education','Unmarried','Revenue','Source_of_care',\n                'Poor_vision','Drinking','Hypertension','Family_hypertension','Diabetes',\n                'Family_diabetes','Family_hepatitis','Chronic_fatigue','ALF','ID']\n\ntrain = data.head(train_.shape[0])\ntest = data.tail(data_test.shape[0])\n\n# target_enc_fea_list = ['Drinking', 'Family_hepatitis', 'Diabetes', 'Obesity_waistline',\n#             'Family_hypertension', 'Blood_lipid_abnormality', 'PVD', 'Poor_vision', \n#             'Education', 'ALF', 'Unmarried', 'Area', 'Chronic_fatigue', \n#             'Source_of_care', 'Hypertension', 'Gender', 'Family_diabetes']\n\n# def add_noise(series, noise_level):\n#     return series * (1 + noise_level * np.random.randn(len(series)))\n\n# def target_encode(trn_series=None, \n#                   tst_series=None, \n#                   target=None, \n#                   min_samples_leaf=1, \n#                   smoothing=1,\n#                   noise_level=0):\n#     assert len(trn_series) == len(target)\n#     assert trn_series.name == tst_series.name\n#     temp = pd.concat([trn_series, target], axis=1)\n#     # Compute target mean \n#     averages = temp.groupby(by=trn_series.name)[target.name].agg([\"mean\", \"count\"])\n#     # Compute smoothing\n#     smoothing = 1 / (1 + np.exp(-(averages[\"count\"] - min_samples_leaf) / smoothing))\n#     # Apply average function to all target data\n#     prior = target.mean()\n#     # The bigger the count the less full_avg is taken into account\n#     averages[target.name] = prior * (1 - smoothing) + averages[\"mean\"] * smoothing\n#     averages.drop([\"mean\", \"count\"], axis=1, inplace=True)\n#     # Apply averages to trn and tst series\n#     ft_trn_series = pd.merge(\n#         trn_series.to_frame(trn_series.name),\n#         averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),\n#         on=trn_series.name,\n#         how='left')['average'].rename(trn_series.name + '_mean').fillna(prior)\n#     # pd.merge does not keep the index so restore it\n#     ft_trn_series.index = trn_series.index \n#     ft_tst_series = pd.merge(\n#         tst_series.to_frame(tst_series.name),\n#         averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),\n#         on=tst_series.name,\n#         how='left')['average'].rename(trn_series.name + '_mean').fillna(prior)\n#     # pd.merge does not keep the index so restore it\n#     ft_tst_series.index = tst_series.index\n#     return add_noise(ft_trn_series, noise_level), add_noise(ft_tst_series, noise_level)\n\n# skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=7410)\n# for j, (train_index, val_index) in enumerate(skf.split(train, label)):\n#     for i in target_enc_fea_list:\n#         train_i, test_i = target_encode(train[i].iloc[train_index], \n#                                         test[i], \n#                                         target=label[train_index], \n#                                         min_samples_leaf=100,\n#                                         smoothing=10,\n#                                         noise_level=0.01)\n#         train[i+'_'+str(j)+'_target_enc'] = train_i\n#         test[i+'_'+str(j)+'_target_enc'] = test_i\n\n\n# for i in target_enc_fea_list:\n#     buf = np.zeros([train.shape[0]])\n#     for j in range(10):\n#         buf = buf+train[i+'_'+str(j)+'_target_enc'].fillna(0).values.flatten()\n#         train = train.drop(columns=[i+'_'+str(j)+'_target_enc'])\n#     train[i+'_target_enc'] = pd.Series(buf/10)\n\n# for i in target_enc_fea_list:\n#     buf = np.zeros([test.shape[0]])\n#     for j in range(10):\n#         buf = buf+test[i+'_'+str(j)+'_target_enc'].fillna(0).values.flatten()\n#         test = test.drop(columns=[i+'_'+str(j)+'_target_enc'])\n#     test[i+'_target_enc'] = pd.Series(buf/10)\n","execution_count":7},{"metadata":{"id":"74AAAB502E614B6A8E75814A15373DBC","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## online test"},{"metadata":{"id":"B4028A471B844C808836C5C93CD407C3","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"import pandas as pd\npre1 = pd.read_csv('/home/kesci/work/test_pre.csv').dropna(axis=0,how='any')\npre1 = pre1['id'].astype(int)\npre2 = pd.read_csv('/home/kesci/work/train_pre.csv').dropna(axis=0,how='any')\npre2 = pre2['id'].astype(int)\npre3 = pre1.append(pre2).values.flatten().tolist()\n\nnow = test['ID'].astype(int).values.flatten().tolist()\nknow_test_id = list(set(now)&set(pre3))\nneed_test_id = list(set(now).difference(set(pre3)))\n\npre1_ = pd.read_csv('/home/kesci/work/test_pre.csv')\npre2_ = pd.read_csv('/home/kesci/work/train_pre.csv')\npre3_ = pre1_.append(pre2_)\n\npre_know_id = pd.DataFrame({'id':know_test_id})\npre_need_id = pd.DataFrame({'ID':need_test_id})\n\npre_konw = pd.merge(pre_know_id, pre3_, on=['id'],how='left')\n\npre_konw.columns = ['ID','hepatitis']\n\nval_online = pd.merge(pre_konw['ID'], test, on=['ID'],how='left').drop(columns=['ID']).fillna(-1)\nval_online_label = pre_konw['hepatitis']\ntest = test.drop(columns=['ID']).fillna(-1)\ntrain = train.drop(columns=['ID']).fillna(-1)\n","execution_count":8},{"metadata":{"id":"1A876579C99A42AF874DED25FC35EFBF","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# CTB"},{"metadata":{"id":"2A03B4987CAB4505B280F029FA41D347","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"from catboost import CatBoostClassifier,CatBoostRegressor\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\n# train_ctb, val_ctb, label_ctb, val_label_ctb = train_test_split(train,label,test_size=0.1, random_state=1234)\n\ncategorical_features_indices = np.where(train.dtypes != np.float)[0]","execution_count":9},{"metadata":{"id":"98A58940C23147EF961EA50A626A3A13","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## 单折"},{"metadata":{"id":"51AE7478F63D48A8843AA775714B3348","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"model = CatBoostClassifier(iterations=500, \n                            depth=10,\n                            border_count=254,\n                            one_hot_max_size=10,\n                            cat_features=categorical_features_indices,\n                            loss_function='Logloss',\n                            eval_metric='AUC',\n                            logging_level='Verbose',\n                            early_stopping_rounds=20,\n                            use_best_model=True,\n                            thread_count=-1,\n                            counter_calc_method='Full')\n\nmodel.fit(train,label,eval_set=(val_online, val_online_label),)#plot=True\n\nans = model.predict_proba(test)","execution_count":7},{"metadata":{"id":"21CED6DA880C488382A4008196E7231E","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## 10折"},{"metadata":{"id":"2A8C0AB679194FBE8C98931196CD8C21","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"cell_type":"code","outputs":[],"source":"ans = np.zeros([test.shape[0]])\nskf = StratifiedKFold(n_splits=10, shuffle=True, random_state=7410)\nfor j, (train_index, val_index) in enumerate(skf.split(train, label)):\n    model = CatBoostClassifier(iterations=500,\n                            depth=10,\n                            border_count=254,\n                            one_hot_max_size=10,\n                            cat_features=categorical_features_indices,\n                            loss_function='Logloss',\n                            eval_metric='AUC',\n                            logging_level='Verbose',\n                            early_stopping_rounds=20,\n                            use_best_model=True,\n                            thread_count=-1,\n                            counter_calc_method='Full')\n\n    model.fit(train.iloc[train_index],label.iloc[train_index],eval_set=(val_online, val_online_label),)#plot=True\n    ans += model.predict_proba(test)[:,1]/10\n","execution_count":null},{"metadata":{"id":"D5586670FD9241D38B61120A2E753565","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## 网格搜索&10折"},{"metadata":{"id":"41C0578255994451A419F40AFCD6A5D5","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":true},"cell_type":"code","outputs":[{"output_type":"stream","text":"0:\ttest: 0.6722182\tbest: 0.6722182 (0)\ttotal: 882ms\tremaining: 7m 20s\n1:\ttest: 0.6764792\tbest: 0.6764792 (1)\ttotal: 1.77s\tremaining: 7m 21s\n2:\ttest: 0.6871451\tbest: 0.6871451 (2)\ttotal: 2.37s\tremaining: 6m 33s\n3:\ttest: 0.6857458\tbest: 0.6871451 (2)\ttotal: 3.27s\tremaining: 6m 45s\n4:\ttest: 0.7492486\tbest: 0.7492486 (4)\ttotal: 3.88s\tremaining: 6m 24s\n5:\ttest: 0.7719303\tbest: 0.7719303 (5)\ttotal: 4.68s\tremaining: 6m 25s\n6:\ttest: 0.7691159\tbest: 0.7719303 (5)\ttotal: 5.47s\tremaining: 6m 25s\n7:\ttest: 0.7619955\tbest: 0.7719303 (5)\ttotal: 6.28s\tremaining: 6m 26s\n8:\ttest: 0.7577728\tbest: 0.7719303 (5)\ttotal: 7.27s\tremaining: 6m 36s\n9:\ttest: 0.7605062\tbest: 0.7719303 (5)\ttotal: 8.08s\tremaining: 6m 35s\n10:\ttest: 0.7556648\tbest: 0.7719303 (5)\ttotal: 9.08s\tremaining: 6m 43s\n11:\ttest: 0.7579055\tbest: 0.7719303 (5)\ttotal: 10.1s\tremaining: 6m 49s\n12:\ttest: 0.7540787\tbest: 0.7719303 (5)\ttotal: 10.9s\tremaining: 6m 47s\n13:\ttest: 0.7539483\tbest: 0.7719303 (5)\ttotal: 11.7s\tremaining: 6m 45s\n14:\ttest: 0.7567019\tbest: 0.7719303 (5)\ttotal: 12.3s\tremaining: 6m 36s\n15:\ttest: 0.7570349\tbest: 0.7719303 (5)\ttotal: 13.1s\tremaining: 6m 35s\n16:\ttest: 0.7562340\tbest: 0.7719303 (5)\ttotal: 13.8s\tremaining: 6m 31s\n17:\ttest: 0.7560495\tbest: 0.7719303 (5)\ttotal: 14.6s\tremaining: 6m 30s\n18:\ttest: 0.7554286\tbest: 0.7719303 (5)\ttotal: 15.3s\tremaining: 6m 26s\n19:\ttest: 0.7536108\tbest: 0.7719303 (5)\ttotal: 16.2s\tremaining: 6m 28s\n20:\ttest: 0.7537503\tbest: 0.7719303 (5)\ttotal: 17s\tremaining: 6m 26s\n21:\ttest: 0.7556940\tbest: 0.7719303 (5)\ttotal: 17.9s\tremaining: 6m 28s\n22:\ttest: 0.7555771\tbest: 0.7719303 (5)\ttotal: 18.6s\tremaining: 6m 25s\n23:\ttest: 0.7590506\tbest: 0.7719303 (5)\ttotal: 19.6s\tremaining: 6m 28s\n24:\ttest: 0.7588616\tbest: 0.7719303 (5)\ttotal: 20.6s\tremaining: 6m 30s\n25:\ttest: 0.7596940\tbest: 0.7719303 (5)\ttotal: 21.2s\tremaining: 6m 25s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7719302587\nbestIteration = 5\n\nShrink model to first 6 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 894ms\tremaining: 7m 26s\n1:\ttest: 0.7367312\tbest: 0.7367312 (1)\ttotal: 1.89s\tremaining: 7m 50s\n2:\ttest: 0.8077458\tbest: 0.8077458 (2)\ttotal: 2.89s\tremaining: 7m 58s\n3:\ttest: 0.8067627\tbest: 0.8077458 (2)\ttotal: 3.7s\tremaining: 7m 38s\n4:\ttest: 0.8303330\tbest: 0.8303330 (4)\ttotal: 4.59s\tremaining: 7m 34s\n5:\ttest: 0.8412171\tbest: 0.8412171 (5)\ttotal: 5.49s\tremaining: 7m 32s\n6:\ttest: 0.8379168\tbest: 0.8412171 (5)\ttotal: 6.09s\tremaining: 7m 8s\n7:\ttest: 0.8389921\tbest: 0.8412171 (5)\ttotal: 6.7s\tremaining: 6m 51s\n8:\ttest: 0.8408414\tbest: 0.8412171 (5)\ttotal: 7.4s\tremaining: 6m 43s\n9:\ttest: 0.8410304\tbest: 0.8412171 (5)\ttotal: 8.2s\tremaining: 6m 41s\n10:\ttest: 0.8410664\tbest: 0.8412171 (5)\ttotal: 8.9s\tremaining: 6m 35s\n11:\ttest: 0.8398133\tbest: 0.8412171 (5)\ttotal: 9.71s\tremaining: 6m 35s\n12:\ttest: 0.8402227\tbest: 0.8412171 (5)\ttotal: 10.4s\tremaining: 6m 30s\n13:\ttest: 0.8412531\tbest: 0.8412531 (13)\ttotal: 11.1s\tremaining: 6m 25s\n14:\ttest: 0.8458875\tbest: 0.8458875 (14)\ttotal: 12s\tremaining: 6m 28s\n15:\ttest: 0.8351316\tbest: 0.8458875 (14)\ttotal: 13.1s\tremaining: 6m 36s\n16:\ttest: 0.8343262\tbest: 0.8458875 (14)\ttotal: 14.1s\tremaining: 6m 40s\n17:\ttest: 0.8399415\tbest: 0.8458875 (14)\ttotal: 14.8s\tremaining: 6m 36s\n18:\ttest: 0.8427447\tbest: 0.8458875 (14)\ttotal: 15.8s\tremaining: 6m 40s\n19:\ttest: 0.8369584\tbest: 0.8458875 (14)\ttotal: 16.7s\tremaining: 6m 40s\n20:\ttest: 0.8377188\tbest: 0.8458875 (14)\ttotal: 17.7s\tremaining: 6m 43s\n21:\ttest: 0.8348256\tbest: 0.8458875 (14)\ttotal: 18.4s\tremaining: 6m 39s\n22:\ttest: 0.8364094\tbest: 0.8458875 (14)\ttotal: 19.3s\tremaining: 6m 40s\n23:\ttest: 0.8360000\tbest: 0.8458875 (14)\ttotal: 20.1s\tremaining: 6m 38s\n24:\ttest: 0.8343802\tbest: 0.8458875 (14)\ttotal: 21s\tremaining: 6m 38s\n25:\ttest: 0.8340112\tbest: 0.8458875 (14)\ttotal: 21.7s\tremaining: 6m 35s\n26:\ttest: 0.8345917\tbest: 0.8458875 (14)\ttotal: 22.6s\tremaining: 6m 35s\n27:\ttest: 0.8359775\tbest: 0.8458875 (14)\ttotal: 23.4s\tremaining: 6m 34s\n28:\ttest: 0.8281800\tbest: 0.8458875 (14)\ttotal: 24.1s\tremaining: 6m 31s\n29:\ttest: 0.8264252\tbest: 0.8458875 (14)\ttotal: 25s\tremaining: 6m 31s\n30:\ttest: 0.8262992\tbest: 0.8458875 (14)\ttotal: 25.9s\tremaining: 6m 31s\n31:\ttest: 0.8266502\tbest: 0.8458875 (14)\ttotal: 26.9s\tremaining: 6m 33s\n32:\ttest: 0.8268031\tbest: 0.8458875 (14)\ttotal: 27.6s\tremaining: 6m 30s\n33:\ttest: 0.8261372\tbest: 0.8458875 (14)\ttotal: 28.5s\tremaining: 6m 30s\n34:\ttest: 0.8263037\tbest: 0.8458875 (14)\ttotal: 29.4s\tremaining: 6m 30s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8458875141\nbestIteration = 14\n\nShrink model to first 15 iterations.\n0:\ttest: 0.7933476\tbest: 0.7933476 (0)\ttotal: 986ms\tremaining: 8m 12s\n1:\ttest: 0.8045557\tbest: 0.8045557 (1)\ttotal: 1.79s\tremaining: 7m 26s\n2:\ttest: 0.8203757\tbest: 0.8203757 (2)\ttotal: 2.79s\tremaining: 7m 42s\n3:\ttest: 0.8182295\tbest: 0.8203757 (2)\ttotal: 3.6s\tremaining: 7m 25s\n4:\ttest: 0.8205512\tbest: 0.8205512 (4)\ttotal: 4.6s\tremaining: 7m 35s\n5:\ttest: 0.8209539\tbest: 0.8209539 (5)\ttotal: 5.68s\tremaining: 7m 48s\n6:\ttest: 0.8429606\tbest: 0.8429606 (6)\ttotal: 6.49s\tremaining: 7m 36s\n7:\ttest: 0.8451586\tbest: 0.8451586 (7)\ttotal: 7.39s\tremaining: 7m 34s\n8:\ttest: 0.8525174\tbest: 0.8525174 (8)\ttotal: 8.29s\tremaining: 7m 32s\n9:\ttest: 0.8514938\tbest: 0.8525174 (8)\ttotal: 8.99s\tremaining: 7m 20s\n10:\ttest: 0.8524072\tbest: 0.8525174 (8)\ttotal: 10.2s\tremaining: 7m 32s\n11:\ttest: 0.8526817\tbest: 0.8526817 (11)\ttotal: 11.1s\tremaining: 7m 30s\n12:\ttest: 0.8535861\tbest: 0.8535861 (12)\ttotal: 12.1s\tremaining: 7m 32s\n13:\ttest: 0.8537705\tbest: 0.8537705 (13)\ttotal: 13.1s\tremaining: 7m 34s\n14:\ttest: 0.8553116\tbest: 0.8553116 (14)\ttotal: 13.9s\tremaining: 7m 28s\n15:\ttest: 0.8556175\tbest: 0.8556175 (15)\ttotal: 14.9s\tremaining: 7m 30s\n16:\ttest: 0.8528774\tbest: 0.8556175 (15)\ttotal: 15.8s\tremaining: 7m 28s\n17:\ttest: 0.8519820\tbest: 0.8556175 (15)\ttotal: 16.5s\tremaining: 7m 21s\n18:\ttest: 0.8529359\tbest: 0.8556175 (15)\ttotal: 17.5s\tremaining: 7m 22s\n19:\ttest: 0.8517570\tbest: 0.8556175 (15)\ttotal: 18.5s\tremaining: 7m 23s\n20:\ttest: 0.8509561\tbest: 0.8556175 (15)\ttotal: 19.5s\tremaining: 7m 24s\n21:\ttest: 0.8502902\tbest: 0.8556175 (15)\ttotal: 20.5s\tremaining: 7m 25s\n22:\ttest: 0.8513791\tbest: 0.8556175 (15)\ttotal: 21.3s\tremaining: 7m 21s\n23:\ttest: 0.8500787\tbest: 0.8556175 (15)\ttotal: 22s\tremaining: 7m 15s\n24:\ttest: 0.8493048\tbest: 0.8556175 (15)\ttotal: 23s\tremaining: 7m 16s\n25:\ttest: 0.8497998\tbest: 0.8556175 (15)\ttotal: 23.8s\tremaining: 7m 13s\n26:\ttest: 0.8502407\tbest: 0.8556175 (15)\ttotal: 24.7s\tremaining: 7m 12s\n27:\ttest: 0.8503577\tbest: 0.8556175 (15)\ttotal: 25.6s\tremaining: 7m 11s\n28:\ttest: 0.8496648\tbest: 0.8556175 (15)\ttotal: 26.7s\tremaining: 7m 13s\n29:\ttest: 0.8476490\tbest: 0.8556175 (15)\ttotal: 27.9s\tremaining: 7m 16s\n30:\ttest: 0.8485264\tbest: 0.8556175 (15)\ttotal: 28.7s\tremaining: 7m 13s\n31:\ttest: 0.8461552\tbest: 0.8556175 (15)\ttotal: 29.4s\tremaining: 7m 9s\n32:\ttest: 0.8430326\tbest: 0.8556175 (15)\ttotal: 30.4s\tremaining: 7m 9s\n33:\ttest: 0.8439550\tbest: 0.8556175 (15)\ttotal: 31.4s\tremaining: 7m 10s\n34:\ttest: 0.8451159\tbest: 0.8556175 (15)\ttotal: 32.2s\tremaining: 7m 7s\n35:\ttest: 0.8454938\tbest: 0.8556175 (15)\ttotal: 33s\tremaining: 7m 5s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8556175478\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6722182\tbest: 0.6722182 (0)\ttotal: 892ms\tremaining: 7m 24s\n1:\ttest: 0.6768031\tbest: 0.6768031 (1)\ttotal: 1.7s\tremaining: 7m 2s\n2:\ttest: 0.6867807\tbest: 0.6867807 (2)\ttotal: 2.39s\tremaining: 6m 35s\n3:\ttest: 0.6859978\tbest: 0.6867807 (2)\ttotal: 3.39s\tremaining: 7m\n4:\ttest: 0.7369584\tbest: 0.7369584 (4)\ttotal: 4.29s\tremaining: 7m 4s\n5:\ttest: 0.7509359\tbest: 0.7509359 (5)\ttotal: 5.09s\tremaining: 6m 59s\n6:\ttest: 0.7465894\tbest: 0.7509359 (5)\ttotal: 5.78s\tremaining: 6m 47s\n7:\ttest: 0.7462902\tbest: 0.7509359 (5)\ttotal: 6.69s\tremaining: 6m 51s\n8:\ttest: 0.7443555\tbest: 0.7509359 (5)\ttotal: 7.38s\tremaining: 6m 42s\n9:\ttest: 0.7444634\tbest: 0.7509359 (5)\ttotal: 8.28s\tremaining: 6m 45s\n10:\ttest: 0.7461867\tbest: 0.7509359 (5)\ttotal: 8.89s\tremaining: 6m 35s\n11:\ttest: 0.7515973\tbest: 0.7515973 (11)\ttotal: 9.7s\tremaining: 6m 34s\n12:\ttest: 0.7512103\tbest: 0.7515973 (11)\ttotal: 10.5s\tremaining: 6m 32s\n13:\ttest: 0.7515028\tbest: 0.7515973 (11)\ttotal: 11.4s\tremaining: 6m 35s\n14:\ttest: 0.7495996\tbest: 0.7515973 (11)\ttotal: 12.1s\tremaining: 6m 30s\n15:\ttest: 0.7531181\tbest: 0.7531181 (15)\ttotal: 13s\tremaining: 6m 32s\n16:\ttest: 0.7555658\tbest: 0.7555658 (16)\ttotal: 13.7s\tremaining: 6m 28s\n17:\ttest: 0.7558223\tbest: 0.7558223 (17)\ttotal: 14.3s\tremaining: 6m 22s\n18:\ttest: 0.7571114\tbest: 0.7571114 (18)\ttotal: 15.2s\tremaining: 6m 24s\n19:\ttest: 0.7577323\tbest: 0.7577323 (19)\ttotal: 16.1s\tremaining: 6m 26s\n20:\ttest: 0.7594016\tbest: 0.7594016 (20)\ttotal: 16.6s\tremaining: 6m 18s\n21:\ttest: 0.7593611\tbest: 0.7594016 (20)\ttotal: 17.4s\tremaining: 6m 17s\n22:\ttest: 0.7579303\tbest: 0.7594016 (20)\ttotal: 18.2s\tremaining: 6m 17s\n23:\ttest: 0.7583127\tbest: 0.7594016 (20)\ttotal: 19.2s\tremaining: 6m 20s\n24:\ttest: 0.7604769\tbest: 0.7604769 (24)\ttotal: 19.9s\tremaining: 6m 17s\n25:\ttest: 0.7614893\tbest: 0.7614893 (25)\ttotal: 20.7s\tremaining: 6m 16s\n26:\ttest: 0.7603015\tbest: 0.7614893 (25)\ttotal: 21.5s\tremaining: 6m 16s\n27:\ttest: 0.7593206\tbest: 0.7614893 (25)\ttotal: 22.3s\tremaining: 6m 15s\n28:\ttest: 0.7596580\tbest: 0.7614893 (25)\ttotal: 23.1s\tremaining: 6m 14s\n29:\ttest: 0.7609089\tbest: 0.7614893 (25)\ttotal: 24s\tremaining: 6m 15s\n30:\ttest: 0.7612103\tbest: 0.7614893 (25)\ttotal: 24.8s\tremaining: 6m 14s\n31:\ttest: 0.7607424\tbest: 0.7614893 (25)\ttotal: 25.7s\tremaining: 6m 15s\n32:\ttest: 0.7606614\tbest: 0.7614893 (25)\ttotal: 26.5s\tremaining: 6m 14s\n33:\ttest: 0.7615883\tbest: 0.7615883 (33)\ttotal: 27.2s\tremaining: 6m 12s\n34:\ttest: 0.7609269\tbest: 0.7615883 (33)\ttotal: 28s\tremaining: 6m 11s\n35:\ttest: 0.7598830\tbest: 0.7615883 (33)\ttotal: 28.7s\tremaining: 6m 9s\n36:\ttest: 0.7588391\tbest: 0.7615883 (33)\ttotal: 29.4s\tremaining: 6m 7s\n37:\ttest: 0.7597075\tbest: 0.7615883 (33)\ttotal: 30.2s\tremaining: 6m 6s\n38:\ttest: 0.7598740\tbest: 0.7615883 (33)\ttotal: 31s\tremaining: 6m 6s\n39:\ttest: 0.7605264\tbest: 0.7615883 (33)\ttotal: 31.7s\tremaining: 6m 4s\n40:\ttest: 0.7594736\tbest: 0.7615883 (33)\ttotal: 32.6s\tremaining: 6m 4s\n41:\ttest: 0.7602250\tbest: 0.7615883 (33)\ttotal: 33.5s\tremaining: 6m 5s\n42:\ttest: 0.7608324\tbest: 0.7615883 (33)\ttotal: 34.4s\tremaining: 6m 5s\n43:\ttest: 0.7613228\tbest: 0.7615883 (33)\ttotal: 35.5s\tremaining: 6m 7s\n44:\ttest: 0.7614443\tbest: 0.7615883 (33)\ttotal: 36.3s\tremaining: 6m 6s\n45:\ttest: 0.7624792\tbest: 0.7624792 (45)\ttotal: 37.2s\tremaining: 6m 6s\n46:\ttest: 0.7621597\tbest: 0.7624792 (45)\ttotal: 38s\tremaining: 6m 5s\n47:\ttest: 0.7620742\tbest: 0.7624792 (45)\ttotal: 38.8s\tremaining: 6m 5s\n48:\ttest: 0.7633431\tbest: 0.7633431 (48)\ttotal: 39.7s\tremaining: 6m 5s\n49:\ttest: 0.7638785\tbest: 0.7638785 (49)\ttotal: 40.6s\tremaining: 6m 5s\n50:\ttest: 0.7635456\tbest: 0.7638785 (49)\ttotal: 41.3s\tremaining: 6m 3s\n51:\ttest: 0.7655613\tbest: 0.7655613 (51)\ttotal: 42s\tremaining: 6m 1s\n52:\ttest: 0.7646704\tbest: 0.7655613 (51)\ttotal: 42.9s\tremaining: 6m 1s\n53:\ttest: 0.7641575\tbest: 0.7655613 (51)\ttotal: 43.8s\tremaining: 6m 1s\n54:\ttest: 0.7640045\tbest: 0.7655613 (51)\ttotal: 44.6s\tremaining: 6m\n55:\ttest: 0.7642070\tbest: 0.7655613 (51)\ttotal: 45.3s\tremaining: 5m 58s\n56:\ttest: 0.7646479\tbest: 0.7655613 (51)\ttotal: 45.9s\tremaining: 5m 56s\n57:\ttest: 0.7643420\tbest: 0.7655613 (51)\ttotal: 46.7s\tremaining: 5m 55s\n58:\ttest: 0.7650889\tbest: 0.7655613 (51)\ttotal: 47.7s\tremaining: 5m 56s\n59:\ttest: 0.7654758\tbest: 0.7655613 (51)\ttotal: 48.4s\tremaining: 5m 54s\n60:\ttest: 0.7652778\tbest: 0.7655613 (51)\ttotal: 49.2s\tremaining: 5m 53s\n61:\ttest: 0.7655433\tbest: 0.7655613 (51)\ttotal: 50s\tremaining: 5m 53s\n62:\ttest: 0.7647739\tbest: 0.7655613 (51)\ttotal: 50.8s\tremaining: 5m 52s\n63:\ttest: 0.7647064\tbest: 0.7655613 (51)\ttotal: 51.8s\tremaining: 5m 52s\n64:\ttest: 0.7648414\tbest: 0.7655613 (51)\ttotal: 52.7s\tremaining: 5m 52s\n65:\ttest: 0.7658268\tbest: 0.7658268 (65)\ttotal: 53.4s\tremaining: 5m 50s\n66:\ttest: 0.7661417\tbest: 0.7661417 (66)\ttotal: 54.3s\tremaining: 5m 50s\n67:\ttest: 0.7663082\tbest: 0.7663082 (67)\ttotal: 54.9s\tremaining: 5m 48s\n68:\ttest: 0.7667537\tbest: 0.7667537 (68)\ttotal: 55.6s\tremaining: 5m 47s\n69:\ttest: 0.7661237\tbest: 0.7667537 (68)\ttotal: 56.5s\tremaining: 5m 46s\n70:\ttest: 0.7660832\tbest: 0.7667537 (68)\ttotal: 57.1s\tremaining: 5m 44s\n71:\ttest: 0.7658493\tbest: 0.7667537 (68)\ttotal: 57.9s\tremaining: 5m 43s\n72:\ttest: 0.7661957\tbest: 0.7667537 (68)\ttotal: 58.7s\tremaining: 5m 43s\n73:\ttest: 0.7673296\tbest: 0.7673296 (73)\ttotal: 59.5s\tremaining: 5m 42s\n74:\ttest: 0.7672126\tbest: 0.7673296 (73)\ttotal: 1m\tremaining: 5m 40s\n75:\ttest: 0.7673791\tbest: 0.7673791 (75)\ttotal: 1m\tremaining: 5m 40s\n76:\ttest: 0.7675906\tbest: 0.7675906 (76)\ttotal: 1m 1s\tremaining: 5m 39s\n77:\ttest: 0.7676265\tbest: 0.7676265 (77)\ttotal: 1m 2s\tremaining: 5m 39s\n78:\ttest: 0.7677570\tbest: 0.7677570 (78)\ttotal: 1m 3s\tremaining: 5m 37s\n79:\ttest: 0.7680585\tbest: 0.7680585 (79)\ttotal: 1m 4s\tremaining: 5m 36s\n80:\ttest: 0.7679055\tbest: 0.7680585 (79)\ttotal: 1m 4s\tremaining: 5m 36s\n81:\ttest: 0.7677885\tbest: 0.7680585 (79)\ttotal: 1m 5s\tremaining: 5m 34s\n82:\ttest: 0.7679370\tbest: 0.7680585 (79)\ttotal: 1m 6s\tremaining: 5m 33s\n83:\ttest: 0.7678470\tbest: 0.7680585 (79)\ttotal: 1m 7s\tremaining: 5m 32s\n84:\ttest: 0.7672576\tbest: 0.7680585 (79)\ttotal: 1m 7s\tremaining: 5m 31s\n85:\ttest: 0.7675096\tbest: 0.7680585 (79)\ttotal: 1m 8s\tremaining: 5m 31s\n86:\ttest: 0.7672666\tbest: 0.7680585 (79)\ttotal: 1m 9s\tremaining: 5m 29s\n87:\ttest: 0.7677345\tbest: 0.7680585 (79)\ttotal: 1m 10s\tremaining: 5m 28s\n88:\ttest: 0.7675951\tbest: 0.7680585 (79)\ttotal: 1m 10s\tremaining: 5m 27s\n89:\ttest: 0.7676805\tbest: 0.7680585 (79)\ttotal: 1m 11s\tremaining: 5m 26s\n90:\ttest: 0.7676220\tbest: 0.7680585 (79)\ttotal: 1m 12s\tremaining: 5m 25s\n91:\ttest: 0.7666502\tbest: 0.7680585 (79)\ttotal: 1m 13s\tremaining: 5m 24s\n92:\ttest: 0.7680180\tbest: 0.7680585 (79)\ttotal: 1m 13s\tremaining: 5m 23s\n93:\ttest: 0.7680810\tbest: 0.7680810 (93)\ttotal: 1m 14s\tremaining: 5m 22s\n94:\ttest: 0.7680360\tbest: 0.7680810 (93)\ttotal: 1m 15s\tremaining: 5m 22s\n95:\ttest: 0.7675456\tbest: 0.7680810 (93)\ttotal: 1m 16s\tremaining: 5m 21s\n96:\ttest: 0.7671271\tbest: 0.7680810 (93)\ttotal: 1m 17s\tremaining: 5m 20s\n97:\ttest: 0.7669516\tbest: 0.7680810 (93)\ttotal: 1m 17s\tremaining: 5m 19s\n98:\ttest: 0.7675861\tbest: 0.7680810 (93)\ttotal: 1m 18s\tremaining: 5m 18s\n99:\ttest: 0.7673251\tbest: 0.7680810 (93)\ttotal: 1m 19s\tremaining: 5m 17s\n100:\ttest: 0.7676715\tbest: 0.7680810 (93)\ttotal: 1m 20s\tremaining: 5m 16s\n101:\ttest: 0.7671991\tbest: 0.7680810 (93)\ttotal: 1m 20s\tremaining: 5m 15s\n102:\ttest: 0.7671496\tbest: 0.7680810 (93)\ttotal: 1m 21s\tremaining: 5m 14s\n103:\ttest: 0.7672441\tbest: 0.7680810 (93)\ttotal: 1m 22s\tremaining: 5m 13s\n104:\ttest: 0.7669291\tbest: 0.7680810 (93)\ttotal: 1m 23s\tremaining: 5m 12s\n105:\ttest: 0.7667087\tbest: 0.7680810 (93)\ttotal: 1m 23s\tremaining: 5m 12s\n106:\ttest: 0.7662677\tbest: 0.7680810 (93)\ttotal: 1m 24s\tremaining: 5m 11s\n107:\ttest: 0.7656783\tbest: 0.7680810 (93)\ttotal: 1m 25s\tremaining: 5m 10s\n108:\ttest: 0.7652913\tbest: 0.7680810 (93)\ttotal: 1m 26s\tremaining: 5m 10s\n109:\ttest: 0.7655388\tbest: 0.7680810 (93)\ttotal: 1m 27s\tremaining: 5m 10s\n110:\ttest: 0.7655478\tbest: 0.7680810 (93)\ttotal: 1m 28s\tremaining: 5m 8s\n111:\ttest: 0.7657368\tbest: 0.7680810 (93)\ttotal: 1m 29s\tremaining: 5m 8s\n112:\ttest: 0.7656783\tbest: 0.7680810 (93)\ttotal: 1m 29s\tremaining: 5m 7s\n113:\ttest: 0.7648189\tbest: 0.7680810 (93)\ttotal: 1m 30s\tremaining: 5m 6s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7680809899\nbestIteration = 93\n\nShrink model to first 94 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 894ms\tremaining: 7m 25s\n1:\ttest: 0.7362677\tbest: 0.7362677 (1)\ttotal: 1.9s\tremaining: 7m 52s\n2:\ttest: 0.8077953\tbest: 0.8077953 (2)\ttotal: 2.5s\tremaining: 6m 53s\n3:\ttest: 0.8069876\tbest: 0.8077953 (2)\ttotal: 3.19s\tremaining: 6m 35s\n4:\ttest: 0.8283870\tbest: 0.8283870 (4)\ttotal: 3.9s\tremaining: 6m 26s\n5:\ttest: 0.8414083\tbest: 0.8414083 (5)\ttotal: 4.79s\tremaining: 6m 34s\n6:\ttest: 0.8360967\tbest: 0.8414083 (5)\ttotal: 5.49s\tremaining: 6m 26s\n7:\ttest: 0.8379213\tbest: 0.8414083 (5)\ttotal: 6.19s\tremaining: 6m 20s\n8:\ttest: 0.8408729\tbest: 0.8414083 (5)\ttotal: 6.89s\tremaining: 6m 15s\n9:\ttest: 0.8455973\tbest: 0.8455973 (9)\ttotal: 7.59s\tremaining: 6m 11s\n10:\ttest: 0.8433926\tbest: 0.8455973 (9)\ttotal: 8.48s\tremaining: 6m 17s\n11:\ttest: 0.8465939\tbest: 0.8465939 (11)\ttotal: 9.19s\tremaining: 6m 13s\n12:\ttest: 0.8432733\tbest: 0.8465939 (11)\ttotal: 9.79s\tremaining: 6m 6s\n13:\ttest: 0.8441485\tbest: 0.8465939 (11)\ttotal: 10.7s\tremaining: 6m 10s\n14:\ttest: 0.8387222\tbest: 0.8465939 (11)\ttotal: 11.5s\tremaining: 6m 11s\n15:\ttest: 0.8422812\tbest: 0.8465939 (11)\ttotal: 12.6s\tremaining: 6m 20s\n16:\ttest: 0.8425017\tbest: 0.8465939 (11)\ttotal: 13.5s\tremaining: 6m 23s\n17:\ttest: 0.8442880\tbest: 0.8465939 (11)\ttotal: 14.4s\tremaining: 6m 25s\n18:\ttest: 0.8452238\tbest: 0.8465939 (11)\ttotal: 15.2s\tremaining: 6m 24s\n19:\ttest: 0.8458853\tbest: 0.8465939 (11)\ttotal: 16.2s\tremaining: 6m 28s\n20:\ttest: 0.8441305\tbest: 0.8465939 (11)\ttotal: 16.9s\tremaining: 6m 25s\n21:\ttest: 0.8445849\tbest: 0.8465939 (11)\ttotal: 17.7s\tremaining: 6m 24s\n22:\ttest: 0.8450304\tbest: 0.8465939 (11)\ttotal: 18.4s\tremaining: 6m 21s\n23:\ttest: 0.8448009\tbest: 0.8465939 (11)\ttotal: 19s\tremaining: 6m 16s\n24:\ttest: 0.8457953\tbest: 0.8465939 (11)\ttotal: 19.9s\tremaining: 6m 17s\n25:\ttest: 0.8439955\tbest: 0.8465939 (11)\ttotal: 20.7s\tremaining: 6m 16s\n26:\ttest: 0.8448189\tbest: 0.8465939 (11)\ttotal: 21.3s\tremaining: 6m 12s\n27:\ttest: 0.8455928\tbest: 0.8465939 (11)\ttotal: 22.2s\tremaining: 6m 13s\n28:\ttest: 0.8462047\tbest: 0.8465939 (11)\ttotal: 22.9s\tremaining: 6m 11s\n29:\ttest: 0.8466457\tbest: 0.8466457 (29)\ttotal: 23.6s\tremaining: 6m 9s\n30:\ttest: 0.8466997\tbest: 0.8466997 (30)\ttotal: 24.3s\tremaining: 6m 7s\n31:\ttest: 0.8463757\tbest: 0.8466997 (30)\ttotal: 25.1s\tremaining: 6m 6s\n32:\ttest: 0.8463217\tbest: 0.8466997 (30)\ttotal: 25.9s\tremaining: 6m 6s\n33:\ttest: 0.8402385\tbest: 0.8466997 (30)\ttotal: 26.9s\tremaining: 6m 8s\n34:\ttest: 0.8405039\tbest: 0.8466997 (30)\ttotal: 27.5s\tremaining: 6m 5s\n35:\ttest: 0.8411654\tbest: 0.8466997 (30)\ttotal: 28.3s\tremaining: 6m 4s\n36:\ttest: 0.8412733\tbest: 0.8466997 (30)\ttotal: 29s\tremaining: 6m 2s\n37:\ttest: 0.8404184\tbest: 0.8466997 (30)\ttotal: 29.7s\tremaining: 6m 1s\n38:\ttest: 0.8412598\tbest: 0.8466997 (30)\ttotal: 30.4s\tremaining: 5m 59s\n39:\ttest: 0.8414848\tbest: 0.8466997 (30)\ttotal: 31.2s\tremaining: 5m 58s\n40:\ttest: 0.8412913\tbest: 0.8466997 (30)\ttotal: 32s\tremaining: 5m 58s\n41:\ttest: 0.8410799\tbest: 0.8466997 (30)\ttotal: 32.7s\tremaining: 5m 56s\n42:\ttest: 0.8409674\tbest: 0.8466997 (30)\ttotal: 33.6s\tremaining: 5m 56s\n43:\ttest: 0.8403510\tbest: 0.8466997 (30)\ttotal: 34.5s\tremaining: 5m 57s\n44:\ttest: 0.8396805\tbest: 0.8466997 (30)\ttotal: 35.2s\tremaining: 5m 55s\n45:\ttest: 0.8384072\tbest: 0.8466997 (30)\ttotal: 36s\tremaining: 5m 55s\n46:\ttest: 0.8379573\tbest: 0.8466997 (30)\ttotal: 36.8s\tremaining: 5m 54s\n47:\ttest: 0.8380157\tbest: 0.8466997 (30)\ttotal: 37.6s\tremaining: 5m 53s\n48:\ttest: 0.8378493\tbest: 0.8466997 (30)\ttotal: 38.5s\tremaining: 5m 54s\n49:\ttest: 0.8378718\tbest: 0.8466997 (30)\ttotal: 39.4s\tremaining: 5m 54s\n50:\ttest: 0.8372643\tbest: 0.8466997 (30)\ttotal: 40.3s\tremaining: 5m 54s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8466996625\nbestIteration = 30\n\nShrink model to first 31 iterations.\n0:\ttest: 0.7933476\tbest: 0.7933476 (0)\ttotal: 794ms\tremaining: 6m 36s\n1:\ttest: 0.8059460\tbest: 0.8059460 (1)\ttotal: 1.49s\tremaining: 6m 10s\n2:\ttest: 0.8239888\tbest: 0.8239888 (2)\ttotal: 2.28s\tremaining: 6m 17s\n3:\ttest: 0.8223982\tbest: 0.8239888 (2)\ttotal: 2.79s\tremaining: 5m 46s\n4:\ttest: 0.8252373\tbest: 0.8252373 (4)\ttotal: 3.48s\tremaining: 5m 44s\n5:\ttest: 0.8329291\tbest: 0.8329291 (5)\ttotal: 4.38s\tremaining: 6m\n6:\ttest: 0.8393206\tbest: 0.8393206 (6)\ttotal: 5.09s\tremaining: 5m 58s\n7:\ttest: 0.8394691\tbest: 0.8394691 (7)\ttotal: 5.99s\tremaining: 6m 8s\n8:\ttest: 0.8453183\tbest: 0.8453183 (8)\ttotal: 6.69s\tremaining: 6m 5s\n9:\ttest: 0.8433386\tbest: 0.8453183 (8)\ttotal: 7.68s\tremaining: 6m 16s\n10:\ttest: 0.8426907\tbest: 0.8453183 (8)\ttotal: 8.48s\tremaining: 6m 17s\n11:\ttest: 0.8446119\tbest: 0.8453183 (8)\ttotal: 9.28s\tremaining: 6m 17s\n12:\ttest: 0.8446299\tbest: 0.8453183 (8)\ttotal: 9.98s\tremaining: 6m 13s\n13:\ttest: 0.8474736\tbest: 0.8474736 (13)\ttotal: 10.9s\tremaining: 6m 17s\n14:\ttest: 0.8477750\tbest: 0.8477750 (14)\ttotal: 11.5s\tremaining: 6m 11s\n15:\ttest: 0.8478785\tbest: 0.8478785 (15)\ttotal: 12.3s\tremaining: 6m 11s\n16:\ttest: 0.8432846\tbest: 0.8478785 (15)\ttotal: 13.1s\tremaining: 6m 11s\n17:\ttest: 0.8455208\tbest: 0.8478785 (15)\ttotal: 13.9s\tremaining: 6m 11s\n18:\ttest: 0.8398470\tbest: 0.8478785 (15)\ttotal: 14.8s\tremaining: 6m 13s\n19:\ttest: 0.8419438\tbest: 0.8478785 (15)\ttotal: 15.6s\tremaining: 6m 13s\n20:\ttest: 0.8420202\tbest: 0.8478785 (15)\ttotal: 16.5s\tremaining: 6m 15s\n21:\ttest: 0.8430776\tbest: 0.8478785 (15)\ttotal: 17.2s\tremaining: 6m 13s\n22:\ttest: 0.8422767\tbest: 0.8478785 (15)\ttotal: 18s\tremaining: 6m 12s\n23:\ttest: 0.8406074\tbest: 0.8478785 (15)\ttotal: 18.8s\tremaining: 6m 12s\n24:\ttest: 0.8334893\tbest: 0.8478785 (15)\ttotal: 19.5s\tremaining: 6m 10s\n25:\ttest: 0.8336423\tbest: 0.8478785 (15)\ttotal: 20.2s\tremaining: 6m 7s\n26:\ttest: 0.8335973\tbest: 0.8478785 (15)\ttotal: 20.9s\tremaining: 6m 5s\n27:\ttest: 0.8342497\tbest: 0.8478785 (15)\ttotal: 21.6s\tremaining: 6m 3s\n28:\ttest: 0.8331789\tbest: 0.8478785 (15)\ttotal: 22.4s\tremaining: 6m 3s\n29:\ttest: 0.8339798\tbest: 0.8478785 (15)\ttotal: 23.2s\tremaining: 6m 3s\n30:\ttest: 0.8326344\tbest: 0.8478785 (15)\ttotal: 24s\tremaining: 6m 2s\n31:\ttest: 0.8319955\tbest: 0.8478785 (15)\ttotal: 24.7s\tremaining: 6m\n32:\ttest: 0.8317300\tbest: 0.8478785 (15)\ttotal: 25.5s\tremaining: 6m\n33:\ttest: 0.8300337\tbest: 0.8478785 (15)\ttotal: 26.3s\tremaining: 6m\n34:\ttest: 0.8301822\tbest: 0.8478785 (15)\ttotal: 27.2s\tremaining: 6m\n35:\ttest: 0.8315546\tbest: 0.8478785 (15)\ttotal: 28s\tremaining: 6m\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8478785152\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6722182\tbest: 0.6722182 (0)\ttotal: 597ms\tremaining: 4m 57s\n1:\ttest: 0.6769336\tbest: 0.6769336 (1)\ttotal: 1.4s\tremaining: 5m 47s\n2:\ttest: 0.7051451\tbest: 0.7051451 (2)\ttotal: 1.99s\tremaining: 5m 29s\n3:\ttest: 0.7021350\tbest: 0.7051451 (2)\ttotal: 2.79s\tremaining: 5m 46s\n4:\ttest: 0.7436490\tbest: 0.7436490 (4)\ttotal: 3.59s\tremaining: 5m 55s\n5:\ttest: 0.7460292\tbest: 0.7460292 (5)\ttotal: 4.29s\tremaining: 5m 53s\n6:\ttest: 0.7452486\tbest: 0.7460292 (5)\ttotal: 5.28s\tremaining: 6m 12s\n7:\ttest: 0.7488436\tbest: 0.7488436 (7)\ttotal: 6.08s\tremaining: 6m 13s\n8:\ttest: 0.7421732\tbest: 0.7488436 (7)\ttotal: 6.69s\tremaining: 6m 5s\n9:\ttest: 0.7502160\tbest: 0.7502160 (9)\ttotal: 7.5s\tremaining: 6m 7s\n10:\ttest: 0.7499258\tbest: 0.7502160 (9)\ttotal: 8.4s\tremaining: 6m 13s\n11:\ttest: 0.7483555\tbest: 0.7502160 (9)\ttotal: 9.2s\tremaining: 6m 14s\n12:\ttest: 0.7539798\tbest: 0.7539798 (12)\ttotal: 9.99s\tremaining: 6m 14s\n13:\ttest: 0.7609066\tbest: 0.7609066 (13)\ttotal: 10.9s\tremaining: 6m 18s\n14:\ttest: 0.7613948\tbest: 0.7613948 (14)\ttotal: 11.8s\tremaining: 6m 21s\n15:\ttest: 0.7588886\tbest: 0.7613948 (14)\ttotal: 12.9s\tremaining: 6m 30s\n16:\ttest: 0.7618223\tbest: 0.7618223 (16)\ttotal: 13.8s\tremaining: 6m 32s\n17:\ttest: 0.7600675\tbest: 0.7618223 (16)\ttotal: 14.7s\tremaining: 6m 33s\n18:\ttest: 0.7604184\tbest: 0.7618223 (16)\ttotal: 15.5s\tremaining: 6m 32s\n19:\ttest: 0.7657728\tbest: 0.7657728 (19)\ttotal: 16.3s\tremaining: 6m 30s\n20:\ttest: 0.7649224\tbest: 0.7657728 (19)\ttotal: 17.1s\tremaining: 6m 29s\n21:\ttest: 0.7652868\tbest: 0.7657728 (19)\ttotal: 17.8s\tremaining: 6m 26s\n22:\ttest: 0.7646389\tbest: 0.7657728 (19)\ttotal: 18.6s\tremaining: 6m 25s\n23:\ttest: 0.7658718\tbest: 0.7658718 (23)\ttotal: 19.2s\tremaining: 6m 20s\n24:\ttest: 0.7649269\tbest: 0.7658718 (23)\ttotal: 20.1s\tremaining: 6m 21s\n25:\ttest: 0.7651024\tbest: 0.7658718 (23)\ttotal: 20.9s\tremaining: 6m 20s\n26:\ttest: 0.7646659\tbest: 0.7658718 (23)\ttotal: 21.7s\tremaining: 6m 19s\n27:\ttest: 0.7637885\tbest: 0.7658718 (23)\ttotal: 22.7s\tremaining: 6m 22s\n28:\ttest: 0.7640990\tbest: 0.7658718 (23)\ttotal: 23.6s\tremaining: 6m 23s\n29:\ttest: 0.7638740\tbest: 0.7658718 (23)\ttotal: 24.5s\tremaining: 6m 23s\n30:\ttest: 0.7628346\tbest: 0.7658718 (23)\ttotal: 25.3s\tremaining: 6m 22s\n31:\ttest: 0.7622002\tbest: 0.7658718 (23)\ttotal: 26.2s\tremaining: 6m 23s\n32:\ttest: 0.7619123\tbest: 0.7658718 (23)\ttotal: 26.9s\tremaining: 6m 20s\n33:\ttest: 0.7616333\tbest: 0.7658718 (23)\ttotal: 27.6s\tremaining: 6m 18s\n34:\ttest: 0.7622182\tbest: 0.7658718 (23)\ttotal: 28.6s\tremaining: 6m 19s\n35:\ttest: 0.7621687\tbest: 0.7658718 (23)\ttotal: 29.5s\tremaining: 6m 20s\n36:\ttest: 0.7632036\tbest: 0.7658718 (23)\ttotal: 30.4s\tremaining: 6m 20s\n37:\ttest: 0.7629561\tbest: 0.7658718 (23)\ttotal: 31.3s\tremaining: 6m 20s\n38:\ttest: 0.7628751\tbest: 0.7658718 (23)\ttotal: 32s\tremaining: 6m 17s\n39:\ttest: 0.7630686\tbest: 0.7658718 (23)\ttotal: 32.9s\tremaining: 6m 18s\n40:\ttest: 0.7616423\tbest: 0.7658718 (23)\ttotal: 33.5s\tremaining: 6m 14s\n41:\ttest: 0.7620157\tbest: 0.7658718 (23)\ttotal: 34.4s\tremaining: 6m 14s\n42:\ttest: 0.7622452\tbest: 0.7658718 (23)\ttotal: 35.3s\tremaining: 6m 14s\n43:\ttest: 0.7624747\tbest: 0.7658718 (23)\ttotal: 36.2s\tremaining: 6m 14s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.765871766\nbestIteration = 23\n\nShrink model to first 24 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 512ms\tremaining: 4m 15s\n1:\ttest: 0.7368256\tbest: 0.7368256 (1)\ttotal: 1.3s\tremaining: 5m 23s\n2:\ttest: 0.8122272\tbest: 0.8122272 (2)\ttotal: 2.1s\tremaining: 5m 47s\n3:\ttest: 0.8150686\tbest: 0.8150686 (3)\ttotal: 2.9s\tremaining: 5m 59s\n4:\ttest: 0.8311991\tbest: 0.8311991 (4)\ttotal: 3.71s\tremaining: 6m 6s\n5:\ttest: 0.8457053\tbest: 0.8457053 (5)\ttotal: 4.4s\tremaining: 6m 2s\n6:\ttest: 0.8420225\tbest: 0.8457053 (5)\ttotal: 5.2s\tremaining: 6m 6s\n7:\ttest: 0.8418403\tbest: 0.8457053 (5)\ttotal: 5.91s\tremaining: 6m 3s\n8:\ttest: 0.8381530\tbest: 0.8457053 (5)\ttotal: 6.79s\tremaining: 6m 10s\n9:\ttest: 0.8408324\tbest: 0.8457053 (5)\ttotal: 7.61s\tremaining: 6m 12s\n10:\ttest: 0.8424477\tbest: 0.8457053 (5)\ttotal: 8.3s\tremaining: 6m 9s\n11:\ttest: 0.8420495\tbest: 0.8457053 (5)\ttotal: 9.19s\tremaining: 6m 13s\n12:\ttest: 0.8449066\tbest: 0.8457053 (5)\ttotal: 10.1s\tremaining: 6m 18s\n13:\ttest: 0.8441485\tbest: 0.8457053 (5)\ttotal: 10.9s\tremaining: 6m 18s\n14:\ttest: 0.8417143\tbest: 0.8457053 (5)\ttotal: 11.8s\tremaining: 6m 21s\n15:\ttest: 0.8397480\tbest: 0.8457053 (5)\ttotal: 12.3s\tremaining: 6m 12s\n16:\ttest: 0.8381147\tbest: 0.8457053 (5)\ttotal: 13.1s\tremaining: 6m 12s\n17:\ttest: 0.8308481\tbest: 0.8457053 (5)\ttotal: 13.9s\tremaining: 6m 12s\n18:\ttest: 0.8283690\tbest: 0.8457053 (5)\ttotal: 14.8s\tremaining: 6m 14s\n19:\ttest: 0.8272981\tbest: 0.8457053 (5)\ttotal: 15.6s\tremaining: 6m 14s\n20:\ttest: 0.8255298\tbest: 0.8457053 (5)\ttotal: 16.3s\tremaining: 6m 11s\n21:\ttest: 0.8252643\tbest: 0.8457053 (5)\ttotal: 17.1s\tremaining: 6m 11s\n22:\ttest: 0.8228031\tbest: 0.8457053 (5)\ttotal: 17.9s\tremaining: 6m 11s\n23:\ttest: 0.8226367\tbest: 0.8457053 (5)\ttotal: 18.8s\tremaining: 6m 12s\n24:\ttest: 0.8218763\tbest: 0.8457053 (5)\ttotal: 19.7s\tremaining: 6m 14s\n25:\ttest: 0.8212328\tbest: 0.8457053 (5)\ttotal: 20.1s\tremaining: 6m 6s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8457052868\nbestIteration = 5\n\nShrink model to first 6 iterations.\n0:\ttest: 0.7933476\tbest: 0.7933476 (0)\ttotal: 898ms\tremaining: 7m 28s\n1:\ttest: 0.8061215\tbest: 0.8061215 (1)\ttotal: 1.8s\tremaining: 7m 27s\n2:\ttest: 0.8213768\tbest: 0.8213768 (2)\ttotal: 2.5s\tremaining: 6m 53s\n3:\ttest: 0.8197458\tbest: 0.8213768 (2)\ttotal: 3.49s\tremaining: 7m 12s\n4:\ttest: 0.8201057\tbest: 0.8213768 (2)\ttotal: 4.28s\tremaining: 7m 4s\n5:\ttest: 0.8158358\tbest: 0.8213768 (2)\ttotal: 5s\tremaining: 6m 51s\n6:\ttest: 0.8182092\tbest: 0.8213768 (2)\ttotal: 5.88s\tremaining: 6m 54s\n7:\ttest: 0.8037368\tbest: 0.8213768 (2)\ttotal: 6.59s\tremaining: 6m 45s\n8:\ttest: 0.8093746\tbest: 0.8213768 (2)\ttotal: 7.5s\tremaining: 6m 49s\n9:\ttest: 0.8225242\tbest: 0.8225242 (9)\ttotal: 8.3s\tremaining: 6m 46s\n10:\ttest: 0.8232418\tbest: 0.8232418 (10)\ttotal: 9.09s\tremaining: 6m 44s\n11:\ttest: 0.8223937\tbest: 0.8232418 (10)\ttotal: 10.2s\tremaining: 6m 54s\n12:\ttest: 0.8267537\tbest: 0.8267537 (12)\ttotal: 11.2s\tremaining: 6m 59s\n13:\ttest: 0.8278245\tbest: 0.8278245 (13)\ttotal: 12.1s\tremaining: 6m 59s\n14:\ttest: 0.8282115\tbest: 0.8282115 (14)\ttotal: 13s\tremaining: 6m 59s\n15:\ttest: 0.8287154\tbest: 0.8287154 (15)\ttotal: 13.8s\tremaining: 6m 57s\n16:\ttest: 0.8311181\tbest: 0.8311181 (16)\ttotal: 14.6s\tremaining: 6m 54s\n17:\ttest: 0.8316580\tbest: 0.8316580 (17)\ttotal: 15.5s\tremaining: 6m 54s\n18:\ttest: 0.8296288\tbest: 0.8316580 (17)\ttotal: 16.3s\tremaining: 6m 52s\n19:\ttest: 0.8279280\tbest: 0.8316580 (17)\ttotal: 17.1s\tremaining: 6m 50s\n20:\ttest: 0.8285354\tbest: 0.8316580 (17)\ttotal: 18s\tremaining: 6m 50s\n21:\ttest: 0.8237345\tbest: 0.8316580 (17)\ttotal: 19s\tremaining: 6m 52s\n22:\ttest: 0.8228526\tbest: 0.8316580 (17)\ttotal: 19.9s\tremaining: 6m 52s\n23:\ttest: 0.8227267\tbest: 0.8316580 (17)\ttotal: 20.5s\tremaining: 6m 46s\n24:\ttest: 0.8236715\tbest: 0.8316580 (17)\ttotal: 21.5s\tremaining: 6m 48s\n25:\ttest: 0.8237885\tbest: 0.8316580 (17)\ttotal: 22.4s\tremaining: 6m 48s\n26:\ttest: 0.8237885\tbest: 0.8316580 (17)\ttotal: 23.2s\tremaining: 6m 46s\n27:\ttest: 0.8237255\tbest: 0.8316580 (17)\ttotal: 24s\tremaining: 6m 44s\n28:\ttest: 0.8210619\tbest: 0.8316580 (17)\ttotal: 24.7s\tremaining: 6m 41s\n29:\ttest: 0.8206029\tbest: 0.8316580 (17)\ttotal: 25.7s\tremaining: 6m 42s\n30:\ttest: 0.8203105\tbest: 0.8316580 (17)\ttotal: 26.4s\tremaining: 6m 39s\n31:\ttest: 0.8212193\tbest: 0.8316580 (17)\ttotal: 27.4s\tremaining: 6m 40s\n32:\ttest: 0.8223037\tbest: 0.8316580 (17)\ttotal: 28.4s\tremaining: 6m 41s\n33:\ttest: 0.8226097\tbest: 0.8316580 (17)\ttotal: 29.3s\tremaining: 6m 41s\n34:\ttest: 0.8238605\tbest: 0.8316580 (17)\ttotal: 30.2s\tremaining: 6m 41s\n35:\ttest: 0.8140517\tbest: 0.8316580 (17)\ttotal: 31.1s\tremaining: 6m 40s\n36:\ttest: 0.8139393\tbest: 0.8316580 (17)\ttotal: 31.9s\tremaining: 6m 39s\n37:\ttest: 0.8138313\tbest: 0.8316580 (17)\ttotal: 32.7s\tremaining: 6m 37s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8316580427\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.6738470\tbest: 0.6738470 (0)\ttotal: 796ms\tremaining: 6m 37s\n1:\ttest: 0.6799483\tbest: 0.6799483 (1)\ttotal: 1.7s\tremaining: 7m 3s\n2:\ttest: 0.6879595\tbest: 0.6879595 (2)\ttotal: 2.7s\tremaining: 7m 27s\n3:\ttest: 0.6881305\tbest: 0.6881305 (3)\ttotal: 3.7s\tremaining: 7m 38s\n4:\ttest: 0.7466277\tbest: 0.7466277 (4)\ttotal: 4.39s\tremaining: 7m 15s\n5:\ttest: 0.7692913\tbest: 0.7692913 (5)\ttotal: 5.2s\tremaining: 7m 7s\n6:\ttest: 0.7681485\tbest: 0.7692913 (5)\ttotal: 6.29s\tremaining: 7m 23s\n7:\ttest: 0.7656535\tbest: 0.7692913 (5)\ttotal: 7s\tremaining: 7m 10s\n8:\ttest: 0.7599505\tbest: 0.7692913 (5)\ttotal: 7.79s\tremaining: 7m 5s\n9:\ttest: 0.7616738\tbest: 0.7692913 (5)\ttotal: 8.8s\tremaining: 7m 11s\n10:\ttest: 0.7577998\tbest: 0.7692913 (5)\ttotal: 9.81s\tremaining: 7m 16s\n11:\ttest: 0.7624139\tbest: 0.7692913 (5)\ttotal: 10.6s\tremaining: 7m 11s\n12:\ttest: 0.7602272\tbest: 0.7692913 (5)\ttotal: 11.5s\tremaining: 7m 11s\n13:\ttest: 0.7605287\tbest: 0.7692913 (5)\ttotal: 12.6s\tremaining: 7m 17s\n14:\ttest: 0.7638853\tbest: 0.7692913 (5)\ttotal: 13.5s\tremaining: 7m 16s\n15:\ttest: 0.7630079\tbest: 0.7692913 (5)\ttotal: 14.3s\tremaining: 7m 12s\n16:\ttest: 0.7600247\tbest: 0.7692913 (5)\ttotal: 15.2s\tremaining: 7m 12s\n17:\ttest: 0.7596378\tbest: 0.7692913 (5)\ttotal: 16.2s\tremaining: 7m 14s\n18:\ttest: 0.7606187\tbest: 0.7692913 (5)\ttotal: 17.2s\tremaining: 7m 15s\n19:\ttest: 0.7590664\tbest: 0.7692913 (5)\ttotal: 18.1s\tremaining: 7m 14s\n20:\ttest: 0.7612013\tbest: 0.7692913 (5)\ttotal: 18.9s\tremaining: 7m 11s\n21:\ttest: 0.7619978\tbest: 0.7692913 (5)\ttotal: 19.7s\tremaining: 7m 8s\n22:\ttest: 0.7614938\tbest: 0.7692913 (5)\ttotal: 20.5s\tremaining: 7m 5s\n23:\ttest: 0.7635276\tbest: 0.7692913 (5)\ttotal: 21.3s\tremaining: 7m 2s\n24:\ttest: 0.7635231\tbest: 0.7692913 (5)\ttotal: 22.2s\tremaining: 7m 1s\n25:\ttest: 0.7645039\tbest: 0.7692913 (5)\ttotal: 23.1s\tremaining: 7m 1s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7692913386\nbestIteration = 5\n\nShrink model to first 6 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 996ms\tremaining: 8m 16s\n1:\ttest: 0.7354938\tbest: 0.7354938 (1)\ttotal: 1.71s\tremaining: 7m 5s\n2:\ttest: 0.8068954\tbest: 0.8068954 (2)\ttotal: 2.81s\tremaining: 7m 45s\n3:\ttest: 0.8053993\tbest: 0.8068954 (2)\ttotal: 3.8s\tremaining: 7m 50s\n4:\ttest: 0.8324049\tbest: 0.8324049 (4)\ttotal: 4.7s\tremaining: 7m 45s\n5:\ttest: 0.8420180\tbest: 0.8420180 (5)\ttotal: 5.4s\tremaining: 7m 24s\n6:\ttest: 0.8402430\tbest: 0.8420180 (5)\ttotal: 6.3s\tremaining: 7m 23s\n7:\ttest: 0.8400990\tbest: 0.8420180 (5)\ttotal: 7.3s\tremaining: 7m 28s\n8:\ttest: 0.8416985\tbest: 0.8420180 (5)\ttotal: 8.2s\tremaining: 7m 27s\n9:\ttest: 0.8418875\tbest: 0.8420180 (5)\ttotal: 8.99s\tremaining: 7m 20s\n10:\ttest: 0.8475073\tbest: 0.8475073 (10)\ttotal: 10s\tremaining: 7m 24s\n11:\ttest: 0.8474016\tbest: 0.8475073 (10)\ttotal: 10.9s\tremaining: 7m 23s\n12:\ttest: 0.8475951\tbest: 0.8475951 (12)\ttotal: 11.8s\tremaining: 7m 21s\n13:\ttest: 0.8477030\tbest: 0.8477030 (13)\ttotal: 12.8s\tremaining: 7m 24s\n14:\ttest: 0.8472441\tbest: 0.8477030 (13)\ttotal: 13.7s\tremaining: 7m 22s\n15:\ttest: 0.8493813\tbest: 0.8493813 (15)\ttotal: 14.6s\tremaining: 7m 21s\n16:\ttest: 0.8510641\tbest: 0.8510641 (16)\ttotal: 15.4s\tremaining: 7m 17s\n17:\ttest: 0.8547717\tbest: 0.8547717 (17)\ttotal: 16.4s\tremaining: 7m 19s\n18:\ttest: 0.8545917\tbest: 0.8547717 (17)\ttotal: 17.3s\tremaining: 7m 17s\n19:\ttest: 0.8500697\tbest: 0.8547717 (17)\ttotal: 17.9s\tremaining: 7m 9s\n20:\ttest: 0.8508571\tbest: 0.8547717 (17)\ttotal: 18.9s\tremaining: 7m 10s\n21:\ttest: 0.8493723\tbest: 0.8547717 (17)\ttotal: 19.8s\tremaining: 7m 9s\n22:\ttest: 0.8513341\tbest: 0.8547717 (17)\ttotal: 20.6s\tremaining: 7m 7s\n23:\ttest: 0.8512756\tbest: 0.8547717 (17)\ttotal: 21.4s\tremaining: 7m 4s\n24:\ttest: 0.8512531\tbest: 0.8547717 (17)\ttotal: 22.1s\tremaining: 6m 59s\n25:\ttest: 0.8520090\tbest: 0.8547717 (17)\ttotal: 23s\tremaining: 6m 59s\n26:\ttest: 0.8523600\tbest: 0.8547717 (17)\ttotal: 24s\tremaining: 7m\n27:\ttest: 0.8520045\tbest: 0.8547717 (17)\ttotal: 24.9s\tremaining: 6m 59s\n28:\ttest: 0.8488819\tbest: 0.8547717 (17)\ttotal: 25.8s\tremaining: 6m 58s\n29:\ttest: 0.8465962\tbest: 0.8547717 (17)\ttotal: 26.9s\tremaining: 7m 1s\n30:\ttest: 0.8463307\tbest: 0.8547717 (17)\ttotal: 27.8s\tremaining: 7m\n31:\ttest: 0.8461822\tbest: 0.8547717 (17)\ttotal: 28.4s\tremaining: 6m 55s\n32:\ttest: 0.8468616\tbest: 0.8547717 (17)\ttotal: 29.4s\tremaining: 6m 55s\n33:\ttest: 0.8468481\tbest: 0.8547717 (17)\ttotal: 30.1s\tremaining: 6m 52s\n34:\ttest: 0.8467087\tbest: 0.8547717 (17)\ttotal: 31s\tremaining: 6m 51s\n35:\ttest: 0.8465377\tbest: 0.8547717 (17)\ttotal: 31.8s\tremaining: 6m 49s\n36:\ttest: 0.8488729\tbest: 0.8547717 (17)\ttotal: 32.6s\tremaining: 6m 47s\n37:\ttest: 0.8486974\tbest: 0.8547717 (17)\ttotal: 33.6s\tremaining: 6m 48s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8547716535\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.7925737\tbest: 0.7925737 (0)\ttotal: 801ms\tremaining: 6m 39s\n1:\ttest: 0.8040787\tbest: 0.8040787 (1)\ttotal: 1.7s\tremaining: 7m 3s\n2:\ttest: 0.8194578\tbest: 0.8194578 (2)\ttotal: 2.6s\tremaining: 7m 11s\n3:\ttest: 0.8186119\tbest: 0.8194578 (2)\ttotal: 3.5s\tremaining: 7m 14s\n4:\ttest: 0.8218650\tbest: 0.8218650 (4)\ttotal: 4.3s\tremaining: 7m 5s\n5:\ttest: 0.8217098\tbest: 0.8218650 (4)\ttotal: 5.1s\tremaining: 7m\n6:\ttest: 0.8429336\tbest: 0.8429336 (6)\ttotal: 5.9s\tremaining: 6m 55s\n7:\ttest: 0.8454376\tbest: 0.8454376 (7)\ttotal: 6.5s\tremaining: 6m 39s\n8:\ttest: 0.8536108\tbest: 0.8536108 (8)\ttotal: 7.39s\tremaining: 6m 43s\n9:\ttest: 0.8531654\tbest: 0.8536108 (8)\ttotal: 8.2s\tremaining: 6m 41s\n10:\ttest: 0.8546772\tbest: 0.8546772 (10)\ttotal: 9.2s\tremaining: 6m 48s\n11:\ttest: 0.8560630\tbest: 0.8560630 (11)\ttotal: 10s\tremaining: 6m 46s\n12:\ttest: 0.8575118\tbest: 0.8575118 (12)\ttotal: 10.9s\tremaining: 6m 48s\n13:\ttest: 0.8567064\tbest: 0.8575118 (12)\ttotal: 11.6s\tremaining: 6m 42s\n14:\ttest: 0.8584612\tbest: 0.8584612 (14)\ttotal: 12.6s\tremaining: 6m 47s\n15:\ttest: 0.8588796\tbest: 0.8588796 (15)\ttotal: 13.5s\tremaining: 6m 48s\n16:\ttest: 0.8573993\tbest: 0.8588796 (15)\ttotal: 14.5s\tremaining: 6m 51s\n17:\ttest: 0.8561530\tbest: 0.8588796 (15)\ttotal: 15.2s\tremaining: 6m 47s\n18:\ttest: 0.8561080\tbest: 0.8588796 (15)\ttotal: 16.1s\tremaining: 6m 47s\n19:\ttest: 0.8546997\tbest: 0.8588796 (15)\ttotal: 16.8s\tremaining: 6m 43s\n20:\ttest: 0.8552846\tbest: 0.8588796 (15)\ttotal: 17.8s\tremaining: 6m 45s\n21:\ttest: 0.8546727\tbest: 0.8588796 (15)\ttotal: 18.7s\tremaining: 6m 46s\n22:\ttest: 0.8552036\tbest: 0.8588796 (15)\ttotal: 19.3s\tremaining: 6m 39s\n23:\ttest: 0.8542677\tbest: 0.8588796 (15)\ttotal: 20.1s\tremaining: 6m 38s\n24:\ttest: 0.8511856\tbest: 0.8588796 (15)\ttotal: 21s\tremaining: 6m 38s\n25:\ttest: 0.8527829\tbest: 0.8588796 (15)\ttotal: 21.8s\tremaining: 6m 37s\n26:\ttest: 0.8515996\tbest: 0.8588796 (15)\ttotal: 22.7s\tremaining: 6m 37s\n27:\ttest: 0.8514826\tbest: 0.8588796 (15)\ttotal: 23.5s\tremaining: 6m 35s\n28:\ttest: 0.8504927\tbest: 0.8588796 (15)\ttotal: 24.3s\tremaining: 6m 34s\n29:\ttest: 0.8494578\tbest: 0.8588796 (15)\ttotal: 25.1s\tremaining: 6m 33s\n30:\ttest: 0.8492283\tbest: 0.8588796 (15)\ttotal: 26s\tremaining: 6m 33s\n31:\ttest: 0.8476355\tbest: 0.8588796 (15)\ttotal: 26.8s\tremaining: 6m 31s\n32:\ttest: 0.8470551\tbest: 0.8588796 (15)\ttotal: 27.8s\tremaining: 6m 33s\n33:\ttest: 0.8471856\tbest: 0.8588796 (15)\ttotal: 28.7s\tremaining: 6m 33s\n34:\ttest: 0.8470956\tbest: 0.8588796 (15)\ttotal: 29.5s\tremaining: 6m 31s\n35:\ttest: 0.8490574\tbest: 0.8588796 (15)\ttotal: 30.4s\tremaining: 6m 31s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.85887964\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6738470\tbest: 0.6738470 (0)\ttotal: 796ms\tremaining: 6m 37s\n1:\ttest: 0.6803127\tbest: 0.6803127 (1)\ttotal: 1.4s\tremaining: 5m 48s\n2:\ttest: 0.6869696\tbest: 0.6869696 (2)\ttotal: 2.2s\tremaining: 6m 4s\n3:\ttest: 0.6821575\tbest: 0.6869696 (2)\ttotal: 3.1s\tremaining: 6m 24s\n4:\ttest: 0.7257278\tbest: 0.7257278 (4)\ttotal: 3.8s\tremaining: 6m 16s\n5:\ttest: 0.7482812\tbest: 0.7482812 (5)\ttotal: 4.8s\tremaining: 6m 35s\n6:\ttest: 0.7476265\tbest: 0.7482812 (5)\ttotal: 5.7s\tremaining: 6m 41s\n7:\ttest: 0.7505489\tbest: 0.7505489 (7)\ttotal: 6.4s\tremaining: 6m 33s\n8:\ttest: 0.7520337\tbest: 0.7520337 (8)\ttotal: 7.1s\tremaining: 6m 27s\n9:\ttest: 0.7571429\tbest: 0.7571429 (9)\ttotal: 8.09s\tremaining: 6m 36s\n10:\ttest: 0.7582475\tbest: 0.7582475 (10)\ttotal: 8.9s\tremaining: 6m 35s\n11:\ttest: 0.7623195\tbest: 0.7623195 (11)\ttotal: 9.79s\tremaining: 6m 38s\n12:\ttest: 0.7573341\tbest: 0.7623195 (11)\ttotal: 10.6s\tremaining: 6m 36s\n13:\ttest: 0.7591654\tbest: 0.7623195 (11)\ttotal: 11.3s\tremaining: 6m 32s\n14:\ttest: 0.7620225\tbest: 0.7623195 (11)\ttotal: 12.2s\tremaining: 6m 34s\n15:\ttest: 0.7622092\tbest: 0.7623195 (11)\ttotal: 12.8s\tremaining: 6m 27s\n16:\ttest: 0.7612778\tbest: 0.7623195 (11)\ttotal: 13.6s\tremaining: 6m 26s\n17:\ttest: 0.7600765\tbest: 0.7623195 (11)\ttotal: 14.3s\tremaining: 6m 22s\n18:\ttest: 0.7611384\tbest: 0.7623195 (11)\ttotal: 15.2s\tremaining: 6m 24s\n19:\ttest: 0.7610214\tbest: 0.7623195 (11)\ttotal: 15.8s\tremaining: 6m 19s\n20:\ttest: 0.7601845\tbest: 0.7623195 (11)\ttotal: 16.5s\tremaining: 6m 16s\n21:\ttest: 0.7595501\tbest: 0.7623195 (11)\ttotal: 17.2s\tremaining: 6m 13s\n22:\ttest: 0.7596670\tbest: 0.7623195 (11)\ttotal: 17.9s\tremaining: 6m 11s\n23:\ttest: 0.7595681\tbest: 0.7623195 (11)\ttotal: 18.7s\tremaining: 6m 10s\n24:\ttest: 0.7600720\tbest: 0.7623195 (11)\ttotal: 19.5s\tremaining: 6m 10s\n25:\ttest: 0.7599685\tbest: 0.7623195 (11)\ttotal: 19.9s\tremaining: 6m 2s\n26:\ttest: 0.7598740\tbest: 0.7623195 (11)\ttotal: 20.8s\tremaining: 6m 4s\n27:\ttest: 0.7584072\tbest: 0.7623195 (11)\ttotal: 21.3s\tremaining: 5m 59s\n28:\ttest: 0.7585062\tbest: 0.7623195 (11)\ttotal: 22.2s\tremaining: 6m\n29:\ttest: 0.7580742\tbest: 0.7623195 (11)\ttotal: 23s\tremaining: 6m\n30:\ttest: 0.7588166\tbest: 0.7623195 (11)\ttotal: 23.6s\tremaining: 5m 56s\n31:\ttest: 0.7602880\tbest: 0.7623195 (11)\ttotal: 24.3s\tremaining: 5m 55s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7623194601\nbestIteration = 11\n\nShrink model to first 12 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 818ms\tremaining: 6m 48s\n1:\ttest: 0.7348999\tbest: 0.7348999 (1)\ttotal: 1.61s\tremaining: 6m 40s\n2:\ttest: 0.8071699\tbest: 0.8071699 (2)\ttotal: 2.4s\tremaining: 6m 38s\n3:\ttest: 0.8056873\tbest: 0.8071699 (2)\ttotal: 3.2s\tremaining: 6m 37s\n4:\ttest: 0.8299820\tbest: 0.8299820 (4)\ttotal: 4.09s\tremaining: 6m 45s\n5:\ttest: 0.8409674\tbest: 0.8409674 (5)\ttotal: 4.99s\tremaining: 6m 51s\n6:\ttest: 0.8408054\tbest: 0.8409674 (5)\ttotal: 5.8s\tremaining: 6m 48s\n7:\ttest: 0.8409944\tbest: 0.8409944 (7)\ttotal: 6.7s\tremaining: 6m 52s\n8:\ttest: 0.8427199\tbest: 0.8427199 (8)\ttotal: 7.5s\tremaining: 6m 49s\n9:\ttest: 0.8427919\tbest: 0.8427919 (9)\ttotal: 8.2s\tremaining: 6m 41s\n10:\ttest: 0.8432441\tbest: 0.8432441 (10)\ttotal: 8.99s\tremaining: 6m 39s\n11:\ttest: 0.8434511\tbest: 0.8434511 (11)\ttotal: 9.8s\tremaining: 6m 38s\n12:\ttest: 0.8449584\tbest: 0.8449584 (12)\ttotal: 10.7s\tremaining: 6m 40s\n13:\ttest: 0.8462002\tbest: 0.8462002 (13)\ttotal: 11.7s\tremaining: 6m 45s\n14:\ttest: 0.8399100\tbest: 0.8462002 (13)\ttotal: 12.5s\tremaining: 6m 44s\n15:\ttest: 0.8435816\tbest: 0.8462002 (13)\ttotal: 13.4s\tremaining: 6m 45s\n16:\ttest: 0.8447784\tbest: 0.8462002 (13)\ttotal: 14.2s\tremaining: 6m 43s\n17:\ttest: 0.8473521\tbest: 0.8473521 (17)\ttotal: 15.1s\tremaining: 6m 44s\n18:\ttest: 0.8496288\tbest: 0.8496288 (18)\ttotal: 16.1s\tremaining: 6m 47s\n19:\ttest: 0.8487289\tbest: 0.8496288 (18)\ttotal: 17s\tremaining: 6m 47s\n20:\ttest: 0.8487469\tbest: 0.8496288 (18)\ttotal: 17.8s\tremaining: 6m 46s\n21:\ttest: 0.8487064\tbest: 0.8496288 (18)\ttotal: 18.6s\tremaining: 6m 44s\n22:\ttest: 0.8501192\tbest: 0.8501192 (22)\ttotal: 19.5s\tremaining: 6m 44s\n23:\ttest: 0.8494893\tbest: 0.8501192 (22)\ttotal: 20.4s\tremaining: 6m 44s\n24:\ttest: 0.8480765\tbest: 0.8501192 (22)\ttotal: 21.3s\tremaining: 6m 44s\n25:\ttest: 0.8478695\tbest: 0.8501192 (22)\ttotal: 21.8s\tremaining: 6m 37s\n26:\ttest: 0.8479370\tbest: 0.8501192 (22)\ttotal: 22.8s\tremaining: 6m 39s\n27:\ttest: 0.8482790\tbest: 0.8501192 (22)\ttotal: 23.5s\tremaining: 6m 36s\n28:\ttest: 0.8480630\tbest: 0.8501192 (22)\ttotal: 24.3s\tremaining: 6m 34s\n29:\ttest: 0.8484904\tbest: 0.8501192 (22)\ttotal: 25.3s\tremaining: 6m 36s\n30:\ttest: 0.8489224\tbest: 0.8501192 (22)\ttotal: 26.1s\tremaining: 6m 34s\n31:\ttest: 0.8478065\tbest: 0.8501192 (22)\ttotal: 26.9s\tremaining: 6m 33s\n32:\ttest: 0.8470416\tbest: 0.8501192 (22)\ttotal: 28s\tremaining: 6m 36s\n33:\ttest: 0.8468841\tbest: 0.8501192 (22)\ttotal: 28.7s\tremaining: 6m 33s\n34:\ttest: 0.8468886\tbest: 0.8501192 (22)\ttotal: 29.6s\tremaining: 6m 33s\n35:\ttest: 0.8467807\tbest: 0.8501192 (22)\ttotal: 30.4s\tremaining: 6m 31s\n36:\ttest: 0.8463037\tbest: 0.8501192 (22)\ttotal: 31.2s\tremaining: 6m 30s\n37:\ttest: 0.8461642\tbest: 0.8501192 (22)\ttotal: 31.9s\tremaining: 6m 27s\n38:\ttest: 0.8457953\tbest: 0.8501192 (22)\ttotal: 32.8s\tremaining: 6m 27s\n39:\ttest: 0.8459078\tbest: 0.8501192 (22)\ttotal: 33.7s\tremaining: 6m 27s\n40:\ttest: 0.8463172\tbest: 0.8501192 (22)\ttotal: 34.5s\tremaining: 6m 26s\n41:\ttest: 0.8409944\tbest: 0.8501192 (22)\ttotal: 35.4s\tremaining: 6m 25s\n42:\ttest: 0.8412823\tbest: 0.8501192 (22)\ttotal: 36.2s\tremaining: 6m 24s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8501192351\nbestIteration = 22\n\nShrink model to first 23 iterations.\n0:\ttest: 0.7925737\tbest: 0.7925737 (0)\ttotal: 895ms\tremaining: 7m 26s\n1:\ttest: 0.8049606\tbest: 0.8049606 (1)\ttotal: 1.7s\tremaining: 7m 2s\n2:\ttest: 0.8197278\tbest: 0.8197278 (2)\ttotal: 2.59s\tremaining: 7m 8s\n3:\ttest: 0.8190754\tbest: 0.8197278 (2)\ttotal: 3.19s\tremaining: 6m 35s\n4:\ttest: 0.8214511\tbest: 0.8214511 (4)\ttotal: 4.08s\tremaining: 6m 44s\n5:\ttest: 0.8208346\tbest: 0.8214511 (4)\ttotal: 4.89s\tremaining: 6m 42s\n6:\ttest: 0.8422182\tbest: 0.8422182 (6)\ttotal: 5.49s\tremaining: 6m 26s\n7:\ttest: 0.8411046\tbest: 0.8422182 (6)\ttotal: 6.29s\tremaining: 6m 26s\n8:\ttest: 0.8468054\tbest: 0.8468054 (8)\ttotal: 6.88s\tremaining: 6m 15s\n9:\ttest: 0.8463757\tbest: 0.8468054 (8)\ttotal: 7.58s\tremaining: 6m 11s\n10:\ttest: 0.8462947\tbest: 0.8468054 (8)\ttotal: 8.29s\tremaining: 6m 8s\n11:\ttest: 0.8482475\tbest: 0.8482475 (11)\ttotal: 8.98s\tremaining: 6m 5s\n12:\ttest: 0.8482340\tbest: 0.8482475 (11)\ttotal: 9.69s\tremaining: 6m 2s\n13:\ttest: 0.8496153\tbest: 0.8496153 (13)\ttotal: 10.6s\tremaining: 6m 7s\n14:\ttest: 0.8513971\tbest: 0.8513971 (14)\ttotal: 11.4s\tremaining: 6m 8s\n15:\ttest: 0.8522115\tbest: 0.8522115 (15)\ttotal: 12.1s\tremaining: 6m 5s\n16:\ttest: 0.8515771\tbest: 0.8522115 (15)\ttotal: 12.8s\tremaining: 6m 3s\n17:\ttest: 0.8511136\tbest: 0.8522115 (15)\ttotal: 13.5s\tremaining: 6m 1s\n18:\ttest: 0.8480540\tbest: 0.8522115 (15)\ttotal: 14.2s\tremaining: 5m 59s\n19:\ttest: 0.8459168\tbest: 0.8522115 (15)\ttotal: 15.1s\tremaining: 6m 2s\n20:\ttest: 0.8453408\tbest: 0.8522115 (15)\ttotal: 15.9s\tremaining: 6m 3s\n21:\ttest: 0.8450709\tbest: 0.8522115 (15)\ttotal: 16.6s\tremaining: 6m\n22:\ttest: 0.8442610\tbest: 0.8522115 (15)\ttotal: 17.4s\tremaining: 6m 1s\n23:\ttest: 0.8442250\tbest: 0.8522115 (15)\ttotal: 18.1s\tremaining: 5m 59s\n24:\ttest: 0.8436445\tbest: 0.8522115 (15)\ttotal: 19.1s\tremaining: 6m 3s\n25:\ttest: 0.8435816\tbest: 0.8522115 (15)\ttotal: 20s\tremaining: 6m 4s\n26:\ttest: 0.8418718\tbest: 0.8522115 (15)\ttotal: 20.9s\tremaining: 6m 6s\n27:\ttest: 0.8413543\tbest: 0.8522115 (15)\ttotal: 21.8s\tremaining: 6m 7s\n28:\ttest: 0.8414038\tbest: 0.8522115 (15)\ttotal: 22.5s\tremaining: 6m 5s\n29:\ttest: 0.8405759\tbest: 0.8522115 (15)\ttotal: 23.3s\tremaining: 6m 5s\n30:\ttest: 0.8411159\tbest: 0.8522115 (15)\ttotal: 24.1s\tremaining: 6m 4s\n31:\ttest: 0.8379123\tbest: 0.8522115 (15)\ttotal: 24.8s\tremaining: 6m 2s\n32:\ttest: 0.8390326\tbest: 0.8522115 (15)\ttotal: 25.4s\tremaining: 5m 59s\n33:\ttest: 0.8388706\tbest: 0.8522115 (15)\ttotal: 26s\tremaining: 5m 56s\n34:\ttest: 0.8369134\tbest: 0.8522115 (15)\ttotal: 26.9s\tremaining: 5m 57s\n35:\ttest: 0.8377323\tbest: 0.8522115 (15)\ttotal: 27.8s\tremaining: 5m 58s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8522114736\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6738470\tbest: 0.6738470 (0)\ttotal: 1.01s\tremaining: 8m 25s\n1:\ttest: 0.6799843\tbest: 0.6799843 (1)\ttotal: 1.81s\tremaining: 7m 31s\n2:\ttest: 0.6710439\tbest: 0.6799843 (1)\ttotal: 2.71s\tremaining: 7m 29s\n3:\ttest: 0.6700742\tbest: 0.6799843 (1)\ttotal: 3.41s\tremaining: 7m 2s\n4:\ttest: 0.7382047\tbest: 0.7382047 (4)\ttotal: 4.3s\tremaining: 7m 5s\n5:\ttest: 0.7385692\tbest: 0.7385692 (5)\ttotal: 4.9s\tremaining: 6m 43s\n6:\ttest: 0.7521710\tbest: 0.7521710 (6)\ttotal: 5.61s\tremaining: 6m 34s\n7:\ttest: 0.7513971\tbest: 0.7521710 (6)\ttotal: 6.5s\tremaining: 6m 40s\n8:\ttest: 0.7518493\tbest: 0.7521710 (6)\ttotal: 7.21s\tremaining: 6m 33s\n9:\ttest: 0.7538898\tbest: 0.7538898 (9)\ttotal: 8.1s\tremaining: 6m 37s\n10:\ttest: 0.7571991\tbest: 0.7571991 (10)\ttotal: 8.9s\tremaining: 6m 35s\n11:\ttest: 0.7584117\tbest: 0.7584117 (11)\ttotal: 9.6s\tremaining: 6m 30s\n12:\ttest: 0.7578898\tbest: 0.7584117 (11)\ttotal: 10.4s\tremaining: 6m 30s\n13:\ttest: 0.7602250\tbest: 0.7602250 (13)\ttotal: 11.2s\tremaining: 6m 28s\n14:\ttest: 0.7638470\tbest: 0.7638470 (14)\ttotal: 12.1s\tremaining: 6m 31s\n15:\ttest: 0.7649404\tbest: 0.7649404 (15)\ttotal: 12.6s\tremaining: 6m 21s\n16:\ttest: 0.7622947\tbest: 0.7649404 (15)\ttotal: 13.3s\tremaining: 6m 17s\n17:\ttest: 0.7616063\tbest: 0.7649404 (15)\ttotal: 14s\tremaining: 6m 15s\n18:\ttest: 0.7601125\tbest: 0.7649404 (15)\ttotal: 14.9s\tremaining: 6m 17s\n19:\ttest: 0.7622317\tbest: 0.7649404 (15)\ttotal: 15.7s\tremaining: 6m 16s\n20:\ttest: 0.7631001\tbest: 0.7649404 (15)\ttotal: 16.4s\tremaining: 6m 14s\n21:\ttest: 0.7653903\tbest: 0.7653903 (21)\ttotal: 17.1s\tremaining: 6m 11s\n22:\ttest: 0.7675546\tbest: 0.7675546 (22)\ttotal: 17.8s\tremaining: 6m 8s\n23:\ttest: 0.7662542\tbest: 0.7675546 (22)\ttotal: 18.7s\tremaining: 6m 10s\n24:\ttest: 0.7656918\tbest: 0.7675546 (22)\ttotal: 19.4s\tremaining: 6m 8s\n25:\ttest: 0.7649089\tbest: 0.7675546 (22)\ttotal: 20.2s\tremaining: 6m 8s\n26:\ttest: 0.7652148\tbest: 0.7675546 (22)\ttotal: 21s\tremaining: 6m 7s\n27:\ttest: 0.7663757\tbest: 0.7675546 (22)\ttotal: 21.9s\tremaining: 6m 8s\n28:\ttest: 0.7688144\tbest: 0.7688144 (28)\ttotal: 22.8s\tremaining: 6m 10s\n29:\ttest: 0.7679820\tbest: 0.7688144 (28)\ttotal: 23.5s\tremaining: 6m 8s\n30:\ttest: 0.7677525\tbest: 0.7688144 (28)\ttotal: 24.4s\tremaining: 6m 8s\n31:\ttest: 0.7679640\tbest: 0.7688144 (28)\ttotal: 25.2s\tremaining: 6m 8s\n32:\ttest: 0.7680900\tbest: 0.7688144 (28)\ttotal: 26.2s\tremaining: 6m 10s\n33:\ttest: 0.7688144\tbest: 0.7688144 (28)\ttotal: 27.1s\tremaining: 6m 11s\n34:\ttest: 0.7689134\tbest: 0.7689134 (34)\ttotal: 28s\tremaining: 6m 11s\n35:\ttest: 0.7695163\tbest: 0.7695163 (35)\ttotal: 28.6s\tremaining: 6m 8s\n36:\ttest: 0.7692598\tbest: 0.7695163 (35)\ttotal: 29.6s\tremaining: 6m 10s\n37:\ttest: 0.7707402\tbest: 0.7707402 (37)\ttotal: 30.3s\tremaining: 6m 8s\n38:\ttest: 0.7701777\tbest: 0.7707402 (37)\ttotal: 31.2s\tremaining: 6m 8s\n39:\ttest: 0.7702137\tbest: 0.7707402 (37)\ttotal: 31.9s\tremaining: 6m 6s\n40:\ttest: 0.7693993\tbest: 0.7707402 (37)\ttotal: 32.8s\tremaining: 6m 7s\n41:\ttest: 0.7688144\tbest: 0.7707402 (37)\ttotal: 33.3s\tremaining: 6m 3s\n42:\ttest: 0.7692778\tbest: 0.7707402 (37)\ttotal: 34.1s\tremaining: 6m 2s\n43:\ttest: 0.7692868\tbest: 0.7707402 (37)\ttotal: 34.8s\tremaining: 6m\n44:\ttest: 0.7687199\tbest: 0.7707402 (37)\ttotal: 35.5s\tremaining: 5m 58s\n45:\ttest: 0.7687694\tbest: 0.7707402 (37)\ttotal: 36.5s\tremaining: 6m\n46:\ttest: 0.7679730\tbest: 0.7707402 (37)\ttotal: 37.2s\tremaining: 5m 58s\n47:\ttest: 0.7685759\tbest: 0.7707402 (37)\ttotal: 37.9s\tremaining: 5m 57s\n48:\ttest: 0.7681305\tbest: 0.7707402 (37)\ttotal: 38.7s\tremaining: 5m 56s\n49:\ttest: 0.7667582\tbest: 0.7707402 (37)\ttotal: 39.5s\tremaining: 5m 55s\n50:\ttest: 0.7672441\tbest: 0.7707402 (37)\ttotal: 40.1s\tremaining: 5m 53s\n51:\ttest: 0.7665152\tbest: 0.7707402 (37)\ttotal: 41s\tremaining: 5m 53s\n52:\ttest: 0.7661462\tbest: 0.7707402 (37)\ttotal: 41.8s\tremaining: 5m 52s\n53:\ttest: 0.7657458\tbest: 0.7707402 (37)\ttotal: 42.7s\tremaining: 5m 52s\n54:\ttest: 0.7655343\tbest: 0.7707402 (37)\ttotal: 43.6s\tremaining: 5m 52s\n55:\ttest: 0.7651204\tbest: 0.7707402 (37)\ttotal: 44.4s\tremaining: 5m 52s\n56:\ttest: 0.7644184\tbest: 0.7707402 (37)\ttotal: 45.1s\tremaining: 5m 50s\n57:\ttest: 0.7648369\tbest: 0.7707402 (37)\ttotal: 45.6s\tremaining: 5m 47s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7707401575\nbestIteration = 37\n\nShrink model to first 38 iterations.\n0:\ttest: 0.6833701\tbest: 0.6833701 (0)\ttotal: 912ms\tremaining: 7m 35s\n1:\ttest: 0.7355163\tbest: 0.7355163 (1)\ttotal: 1.7s\tremaining: 7m 3s\n2:\ttest: 0.8110304\tbest: 0.8110304 (2)\ttotal: 2.42s\tremaining: 6m 40s\n3:\ttest: 0.8141912\tbest: 0.8141912 (3)\ttotal: 3.22s\tremaining: 6m 39s\n4:\ttest: 0.8444049\tbest: 0.8444049 (4)\ttotal: 4.13s\tremaining: 6m 48s\n5:\ttest: 0.8517908\tbest: 0.8517908 (5)\ttotal: 5.22s\tremaining: 7m 9s\n6:\ttest: 0.8448301\tbest: 0.8517908 (5)\ttotal: 6.13s\tremaining: 7m 11s\n7:\ttest: 0.8480045\tbest: 0.8517908 (5)\ttotal: 6.93s\tremaining: 7m 6s\n8:\ttest: 0.8496108\tbest: 0.8517908 (5)\ttotal: 7.82s\tremaining: 7m 6s\n9:\ttest: 0.8545354\tbest: 0.8545354 (9)\ttotal: 8.72s\tremaining: 7m 7s\n10:\ttest: 0.8488436\tbest: 0.8545354 (9)\ttotal: 9.43s\tremaining: 6m 59s\n11:\ttest: 0.8470011\tbest: 0.8545354 (9)\ttotal: 10.3s\tremaining: 6m 59s\n12:\ttest: 0.8458898\tbest: 0.8545354 (9)\ttotal: 11.2s\tremaining: 7m\n13:\ttest: 0.8464072\tbest: 0.8545354 (9)\ttotal: 12.1s\tremaining: 7m\n14:\ttest: 0.8471856\tbest: 0.8545354 (9)\ttotal: 13.1s\tremaining: 7m 4s\n15:\ttest: 0.8466907\tbest: 0.8545354 (9)\ttotal: 14.2s\tremaining: 7m 10s\n16:\ttest: 0.8432666\tbest: 0.8545354 (9)\ttotal: 15.1s\tremaining: 7m 9s\n17:\ttest: 0.8440697\tbest: 0.8545354 (9)\ttotal: 16s\tremaining: 7m 8s\n18:\ttest: 0.8450281\tbest: 0.8545354 (9)\ttotal: 17.1s\tremaining: 7m 13s\n19:\ttest: 0.8444342\tbest: 0.8545354 (9)\ttotal: 18s\tremaining: 7m 12s\n20:\ttest: 0.8432846\tbest: 0.8545354 (9)\ttotal: 18.7s\tremaining: 7m 6s\n21:\ttest: 0.8432621\tbest: 0.8545354 (9)\ttotal: 19.5s\tremaining: 7m 3s\n22:\ttest: 0.8433836\tbest: 0.8545354 (9)\ttotal: 20.4s\tremaining: 7m 3s\n23:\ttest: 0.8430304\tbest: 0.8545354 (9)\ttotal: 21.4s\tremaining: 7m 4s\n24:\ttest: 0.8427019\tbest: 0.8545354 (9)\ttotal: 22.2s\tremaining: 7m 1s\n25:\ttest: 0.8361395\tbest: 0.8545354 (9)\ttotal: 22.9s\tremaining: 6m 57s\n26:\ttest: 0.8358155\tbest: 0.8545354 (9)\ttotal: 23.7s\tremaining: 6m 55s\n27:\ttest: 0.8365219\tbest: 0.8545354 (9)\ttotal: 24.6s\tremaining: 6m 54s\n28:\ttest: 0.8370214\tbest: 0.8545354 (9)\ttotal: 25.6s\tremaining: 6m 55s\n29:\ttest: 0.8368414\tbest: 0.8545354 (9)\ttotal: 26.5s\tremaining: 6m 55s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8545354331\nbestIteration = 9\n\nShrink model to first 10 iterations.\n0:\ttest: 0.7925737\tbest: 0.7925737 (0)\ttotal: 809ms\tremaining: 6m 43s\n1:\ttest: 0.8049381\tbest: 0.8049381 (1)\ttotal: 1.61s\tremaining: 6m 40s\n2:\ttest: 0.8197818\tbest: 0.8197818 (2)\ttotal: 2.59s\tremaining: 7m 9s\n3:\ttest: 0.8194938\tbest: 0.8197818 (2)\ttotal: 3.4s\tremaining: 7m 2s\n4:\ttest: 0.8238785\tbest: 0.8238785 (4)\ttotal: 4.39s\tremaining: 7m 15s\n5:\ttest: 0.8248571\tbest: 0.8248571 (5)\ttotal: 5.3s\tremaining: 7m 16s\n6:\ttest: 0.8307267\tbest: 0.8307267 (6)\ttotal: 6.1s\tremaining: 7m 9s\n7:\ttest: 0.8256873\tbest: 0.8307267 (6)\ttotal: 7s\tremaining: 7m 10s\n8:\ttest: 0.8301417\tbest: 0.8307267 (6)\ttotal: 7.8s\tremaining: 7m 5s\n9:\ttest: 0.8297278\tbest: 0.8307267 (6)\ttotal: 8.8s\tremaining: 7m 11s\n10:\ttest: 0.8230214\tbest: 0.8307267 (6)\ttotal: 9.8s\tremaining: 7m 15s\n11:\ttest: 0.8203645\tbest: 0.8307267 (6)\ttotal: 10.7s\tremaining: 7m 15s\n12:\ttest: 0.8317300\tbest: 0.8317300 (12)\ttotal: 11.4s\tremaining: 7m 7s\n13:\ttest: 0.8333678\tbest: 0.8333678 (13)\ttotal: 12.2s\tremaining: 7m 3s\n14:\ttest: 0.8348391\tbest: 0.8348391 (14)\ttotal: 13.1s\tremaining: 7m 3s\n15:\ttest: 0.8332643\tbest: 0.8348391 (14)\ttotal: 14.2s\tremaining: 7m 9s\n16:\ttest: 0.8351676\tbest: 0.8351676 (16)\ttotal: 15s\tremaining: 7m 6s\n17:\ttest: 0.8369134\tbest: 0.8369134 (17)\ttotal: 15.7s\tremaining: 7m\n18:\ttest: 0.8368639\tbest: 0.8369134 (17)\ttotal: 16.6s\tremaining: 7m\n19:\ttest: 0.8364049\tbest: 0.8369134 (17)\ttotal: 17.3s\tremaining: 6m 55s\n20:\ttest: 0.8360450\tbest: 0.8369134 (17)\ttotal: 18.1s\tremaining: 6m 52s\n21:\ttest: 0.8344342\tbest: 0.8369134 (17)\ttotal: 19.1s\tremaining: 6m 54s\n22:\ttest: 0.8353386\tbest: 0.8369134 (17)\ttotal: 19.7s\tremaining: 6m 48s\n23:\ttest: 0.8357525\tbest: 0.8369134 (17)\ttotal: 20.3s\tremaining: 6m 42s\n24:\ttest: 0.8370754\tbest: 0.8370754 (24)\ttotal: 21.3s\tremaining: 6m 44s\n25:\ttest: 0.8380112\tbest: 0.8380112 (25)\ttotal: 22s\tremaining: 6m 41s\n26:\ttest: 0.8366119\tbest: 0.8380112 (25)\ttotal: 22.9s\tremaining: 6m 40s\n27:\ttest: 0.8371384\tbest: 0.8380112 (25)\ttotal: 23.7s\tremaining: 6m 39s\n28:\ttest: 0.8372958\tbest: 0.8380112 (25)\ttotal: 24.5s\tremaining: 6m 37s\n29:\ttest: 0.8374488\tbest: 0.8380112 (25)\ttotal: 25.4s\tremaining: 6m 37s\n30:\ttest: 0.8291789\tbest: 0.8380112 (25)\ttotal: 26.2s\tremaining: 6m 36s\n31:\ttest: 0.8302092\tbest: 0.8380112 (25)\ttotal: 27s\tremaining: 6m 34s\n32:\ttest: 0.8297008\tbest: 0.8380112 (25)\ttotal: 27.7s\tremaining: 6m 31s\n33:\ttest: 0.8315951\tbest: 0.8380112 (25)\ttotal: 28.5s\tremaining: 6m 30s\n34:\ttest: 0.8315546\tbest: 0.8380112 (25)\ttotal: 29.5s\tremaining: 6m 31s\n35:\ttest: 0.8306907\tbest: 0.8380112 (25)\ttotal: 30.2s\tremaining: 6m 29s\n36:\ttest: 0.8306007\tbest: 0.8380112 (25)\ttotal: 31.1s\tremaining: 6m 28s\n37:\ttest: 0.8316535\tbest: 0.8380112 (25)\ttotal: 31.9s\tremaining: 6m 27s\n38:\ttest: 0.8322025\tbest: 0.8380112 (25)\ttotal: 32.9s\tremaining: 6m 28s\n39:\ttest: 0.8316985\tbest: 0.8380112 (25)\ttotal: 33.7s\tremaining: 6m 27s\n40:\ttest: 0.8321665\tbest: 0.8380112 (25)\ttotal: 34.4s\tremaining: 6m 24s\n41:\ttest: 0.8317840\tbest: 0.8380112 (25)\ttotal: 35.2s\tremaining: 6m 23s\n42:\ttest: 0.8307717\tbest: 0.8380112 (25)\ttotal: 35.9s\tremaining: 6m 21s\n43:\ttest: 0.8308166\tbest: 0.8380112 (25)\ttotal: 36.8s\tremaining: 6m 21s\n44:\ttest: 0.8310101\tbest: 0.8380112 (25)\ttotal: 37.4s\tremaining: 6m 18s\n45:\ttest: 0.8310821\tbest: 0.8380112 (25)\ttotal: 38.4s\tremaining: 6m 18s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8380112486\nbestIteration = 25\n\nShrink model to first 26 iterations.\n0:\ttest: 0.6514488\tbest: 0.6514488 (0)\ttotal: 506ms\tremaining: 4m 12s\n1:\ttest: 0.6705129\tbest: 0.6705129 (1)\ttotal: 1.3s\tremaining: 5m 24s\n2:\ttest: 0.6869651\tbest: 0.6869651 (2)\ttotal: 1.91s\tremaining: 5m 16s\n3:\ttest: 0.6906457\tbest: 0.6906457 (3)\ttotal: 2.7s\tremaining: 5m 35s\n4:\ttest: 0.7517075\tbest: 0.7517075 (4)\ttotal: 3.41s\tremaining: 5m 37s\n5:\ttest: 0.7697548\tbest: 0.7697548 (5)\ttotal: 4.31s\tremaining: 5m 54s\n6:\ttest: 0.7697705\tbest: 0.7697705 (6)\ttotal: 5.11s\tremaining: 6m\n7:\ttest: 0.7696310\tbest: 0.7697705 (6)\ttotal: 5.9s\tremaining: 6m 2s\n8:\ttest: 0.7638650\tbest: 0.7697705 (6)\ttotal: 6.51s\tremaining: 5m 55s\n9:\ttest: 0.7650889\tbest: 0.7697705 (6)\ttotal: 7.3s\tremaining: 5m 57s\n10:\ttest: 0.7623195\tbest: 0.7697705 (6)\ttotal: 8.11s\tremaining: 6m\n11:\ttest: 0.7657705\tbest: 0.7697705 (6)\ttotal: 8.9s\tremaining: 6m 2s\n12:\ttest: 0.7636243\tbest: 0.7697705 (6)\ttotal: 9.81s\tremaining: 6m 7s\n13:\ttest: 0.7638853\tbest: 0.7697705 (6)\ttotal: 10.8s\tremaining: 6m 15s\n14:\ttest: 0.7673768\tbest: 0.7697705 (6)\ttotal: 11.7s\tremaining: 6m 18s\n15:\ttest: 0.7663015\tbest: 0.7697705 (6)\ttotal: 12.5s\tremaining: 6m 18s\n16:\ttest: 0.7629359\tbest: 0.7697705 (6)\ttotal: 13.4s\tremaining: 6m 20s\n17:\ttest: 0.7629449\tbest: 0.7697705 (6)\ttotal: 14.1s\tremaining: 6m 17s\n18:\ttest: 0.7639168\tbest: 0.7697705 (6)\ttotal: 14.9s\tremaining: 6m 17s\n19:\ttest: 0.7625242\tbest: 0.7697705 (6)\ttotal: 15.8s\tremaining: 6m 19s\n20:\ttest: 0.7627492\tbest: 0.7697705 (6)\ttotal: 16.5s\tremaining: 6m 16s\n21:\ttest: 0.7628391\tbest: 0.7697705 (6)\ttotal: 17.2s\tremaining: 6m 13s\n22:\ttest: 0.7621597\tbest: 0.7697705 (6)\ttotal: 18s\tremaining: 6m 13s\n23:\ttest: 0.7638650\tbest: 0.7697705 (6)\ttotal: 18.8s\tremaining: 6m 12s\n24:\ttest: 0.7655433\tbest: 0.7697705 (6)\ttotal: 19.7s\tremaining: 6m 14s\n25:\ttest: 0.7663352\tbest: 0.7697705 (6)\ttotal: 20.6s\tremaining: 6m 15s\n26:\ttest: 0.7673926\tbest: 0.7697705 (6)\ttotal: 21.5s\tremaining: 6m 16s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7697705287\nbestIteration = 6\n\nShrink model to first 7 iterations.\n0:\ttest: 0.6833521\tbest: 0.6833521 (0)\ttotal: 904ms\tremaining: 7m 31s\n1:\ttest: 0.7355118\tbest: 0.7355118 (1)\ttotal: 1.61s\tremaining: 6m 40s\n2:\ttest: 0.8077053\tbest: 0.8077053 (2)\ttotal: 2.31s\tremaining: 6m 21s\n3:\ttest: 0.8056963\tbest: 0.8077053 (2)\ttotal: 3s\tremaining: 6m 12s\n4:\ttest: 0.8381710\tbest: 0.8381710 (4)\ttotal: 3.71s\tremaining: 6m 7s\n5:\ttest: 0.8413273\tbest: 0.8413273 (5)\ttotal: 4.6s\tremaining: 6m 19s\n6:\ttest: 0.8359370\tbest: 0.8413273 (5)\ttotal: 5.3s\tremaining: 6m 13s\n7:\ttest: 0.8399033\tbest: 0.8413273 (5)\ttotal: 6.1s\tremaining: 6m 15s\n8:\ttest: 0.8417503\tbest: 0.8417503 (8)\ttotal: 7.09s\tremaining: 6m 27s\n9:\ttest: 0.8418043\tbest: 0.8418043 (9)\ttotal: 7.9s\tremaining: 6m 27s\n10:\ttest: 0.8480247\tbest: 0.8480247 (10)\ttotal: 8.9s\tremaining: 6m 35s\n11:\ttest: 0.8464319\tbest: 0.8480247 (10)\ttotal: 9.5s\tremaining: 6m 26s\n12:\ttest: 0.8460877\tbest: 0.8480247 (10)\ttotal: 10.1s\tremaining: 6m 18s\n13:\ttest: 0.8456738\tbest: 0.8480247 (10)\ttotal: 10.9s\tremaining: 6m 18s\n14:\ttest: 0.8452373\tbest: 0.8480247 (10)\ttotal: 11.8s\tremaining: 6m 21s\n15:\ttest: 0.8476355\tbest: 0.8480247 (10)\ttotal: 12.7s\tremaining: 6m 24s\n16:\ttest: 0.8499393\tbest: 0.8499393 (16)\ttotal: 13.5s\tremaining: 6m 23s\n17:\ttest: 0.8548121\tbest: 0.8548121 (17)\ttotal: 14.4s\tremaining: 6m 25s\n18:\ttest: 0.8545332\tbest: 0.8548121 (17)\ttotal: 15.1s\tremaining: 6m 22s\n19:\ttest: 0.8504612\tbest: 0.8548121 (17)\ttotal: 16s\tremaining: 6m 23s\n20:\ttest: 0.8512936\tbest: 0.8548121 (17)\ttotal: 16.8s\tremaining: 6m 22s\n21:\ttest: 0.8499708\tbest: 0.8548121 (17)\ttotal: 17.6s\tremaining: 6m 22s\n22:\ttest: 0.8520000\tbest: 0.8548121 (17)\ttotal: 18.5s\tremaining: 6m 23s\n23:\ttest: 0.8511136\tbest: 0.8548121 (17)\ttotal: 19.3s\tremaining: 6m 22s\n24:\ttest: 0.8499303\tbest: 0.8548121 (17)\ttotal: 20.2s\tremaining: 6m 23s\n25:\ttest: 0.8515861\tbest: 0.8548121 (17)\ttotal: 21s\tremaining: 6m 22s\n26:\ttest: 0.8519415\tbest: 0.8548121 (17)\ttotal: 21.8s\tremaining: 6m 21s\n27:\ttest: 0.8514646\tbest: 0.8548121 (17)\ttotal: 22.4s\tremaining: 6m 17s\n28:\ttest: 0.8500652\tbest: 0.8548121 (17)\ttotal: 23.3s\tremaining: 6m 18s\n29:\ttest: 0.8481170\tbest: 0.8548121 (17)\ttotal: 24s\tremaining: 6m 15s\n30:\ttest: 0.8481710\tbest: 0.8548121 (17)\ttotal: 24.7s\tremaining: 6m 13s\n31:\ttest: 0.8482115\tbest: 0.8548121 (17)\ttotal: 25.5s\tremaining: 6m 12s\n32:\ttest: 0.8494263\tbest: 0.8548121 (17)\ttotal: 26.4s\tremaining: 6m 13s\n33:\ttest: 0.8494173\tbest: 0.8548121 (17)\ttotal: 27.3s\tremaining: 6m 13s\n34:\ttest: 0.8488729\tbest: 0.8548121 (17)\ttotal: 28.2s\tremaining: 6m 14s\n35:\ttest: 0.8485129\tbest: 0.8548121 (17)\ttotal: 28.9s\tremaining: 6m 12s\n36:\ttest: 0.8498358\tbest: 0.8548121 (17)\ttotal: 29.7s\tremaining: 6m 11s\n37:\ttest: 0.8496603\tbest: 0.8548121 (17)\ttotal: 30.3s\tremaining: 6m 8s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8548121485\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.7929201\tbest: 0.7929201 (0)\ttotal: 703ms\tremaining: 5m 50s\n1:\ttest: 0.8027829\tbest: 0.8027829 (1)\ttotal: 1.39s\tremaining: 5m 46s\n2:\ttest: 0.8187897\tbest: 0.8187897 (2)\ttotal: 2.09s\tremaining: 5m 46s\n3:\ttest: 0.8185399\tbest: 0.8187897 (2)\ttotal: 2.89s\tremaining: 5m 58s\n4:\ttest: 0.8207717\tbest: 0.8207717 (4)\ttotal: 3.7s\tremaining: 6m 5s\n5:\ttest: 0.8174803\tbest: 0.8207717 (4)\ttotal: 4.4s\tremaining: 6m 1s\n6:\ttest: 0.8393228\tbest: 0.8393228 (6)\ttotal: 5s\tremaining: 5m 51s\n7:\ttest: 0.8399888\tbest: 0.8399888 (7)\ttotal: 5.81s\tremaining: 5m 57s\n8:\ttest: 0.8487492\tbest: 0.8487492 (8)\ttotal: 6.69s\tremaining: 6m 5s\n9:\ttest: 0.8484634\tbest: 0.8487492 (8)\ttotal: 7.49s\tremaining: 6m 7s\n10:\ttest: 0.8466434\tbest: 0.8487492 (8)\ttotal: 8.3s\tremaining: 6m 8s\n11:\ttest: 0.8489021\tbest: 0.8489021 (11)\ttotal: 9.2s\tremaining: 6m 14s\n12:\ttest: 0.8533971\tbest: 0.8533971 (12)\ttotal: 9.99s\tremaining: 6m 14s\n13:\ttest: 0.8526997\tbest: 0.8533971 (12)\ttotal: 10.7s\tremaining: 6m 11s\n14:\ttest: 0.8541912\tbest: 0.8541912 (14)\ttotal: 11.5s\tremaining: 6m 11s\n15:\ttest: 0.8545287\tbest: 0.8545287 (15)\ttotal: 12.2s\tremaining: 6m 8s\n16:\ttest: 0.8530259\tbest: 0.8545287 (15)\ttotal: 13s\tremaining: 6m 8s\n17:\ttest: 0.8537188\tbest: 0.8545287 (15)\ttotal: 13.8s\tremaining: 6m 9s\n18:\ttest: 0.8513656\tbest: 0.8545287 (15)\ttotal: 14.6s\tremaining: 6m 9s\n19:\ttest: 0.8525894\tbest: 0.8545287 (15)\ttotal: 15.5s\tremaining: 6m 11s\n20:\ttest: 0.8523510\tbest: 0.8545287 (15)\ttotal: 16.4s\tremaining: 6m 13s\n21:\ttest: 0.8522925\tbest: 0.8545287 (15)\ttotal: 17.2s\tremaining: 6m 13s\n22:\ttest: 0.8513971\tbest: 0.8545287 (15)\ttotal: 18.2s\tremaining: 6m 17s\n23:\ttest: 0.8481665\tbest: 0.8545287 (15)\ttotal: 19.1s\tremaining: 6m 18s\n24:\ttest: 0.8453768\tbest: 0.8545287 (15)\ttotal: 19.9s\tremaining: 6m 17s\n25:\ttest: 0.8450844\tbest: 0.8545287 (15)\ttotal: 20.7s\tremaining: 6m 17s\n26:\ttest: 0.8452373\tbest: 0.8545287 (15)\ttotal: 21.5s\tremaining: 6m 16s\n27:\ttest: 0.8457548\tbest: 0.8545287 (15)\ttotal: 22.4s\tremaining: 6m 17s\n28:\ttest: 0.8455028\tbest: 0.8545287 (15)\ttotal: 23.2s\tremaining: 6m 16s\n29:\ttest: 0.8451114\tbest: 0.8545287 (15)\ttotal: 23.9s\tremaining: 6m 14s\n30:\ttest: 0.8436805\tbest: 0.8545287 (15)\ttotal: 24.6s\tremaining: 6m 11s\n31:\ttest: 0.8456603\tbest: 0.8545287 (15)\ttotal: 25.6s\tremaining: 6m 14s\n32:\ttest: 0.8460742\tbest: 0.8545287 (15)\ttotal: 26.1s\tremaining: 6m 9s\n33:\ttest: 0.8461147\tbest: 0.8545287 (15)\ttotal: 26.8s\tremaining: 6m 7s\n34:\ttest: 0.8453678\tbest: 0.8545287 (15)\ttotal: 27.6s\tremaining: 6m 6s\n35:\ttest: 0.8453093\tbest: 0.8545287 (15)\ttotal: 28.3s\tremaining: 6m 4s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8545286839\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6514488\tbest: 0.6514488 (0)\ttotal: 798ms\tremaining: 6m 38s\n1:\ttest: 0.6699235\tbest: 0.6699235 (1)\ttotal: 1.7s\tremaining: 7m 3s\n2:\ttest: 0.6862272\tbest: 0.6862272 (2)\ttotal: 2.6s\tremaining: 7m 11s\n3:\ttest: 0.6843600\tbest: 0.6862272 (2)\ttotal: 3.3s\tremaining: 6m 49s\n4:\ttest: 0.7281395\tbest: 0.7281395 (4)\ttotal: 4.1s\tremaining: 6m 45s\n5:\ttest: 0.7523532\tbest: 0.7523532 (5)\ttotal: 5s\tremaining: 6m 51s\n6:\ttest: 0.7527424\tbest: 0.7527424 (6)\ttotal: 5.79s\tremaining: 6m 48s\n7:\ttest: 0.7544409\tbest: 0.7544409 (7)\ttotal: 6.7s\tremaining: 6m 52s\n8:\ttest: 0.7551474\tbest: 0.7551474 (8)\ttotal: 7.31s\tremaining: 6m 38s\n9:\ttest: 0.7590439\tbest: 0.7590439 (9)\ttotal: 8.12s\tremaining: 6m 37s\n10:\ttest: 0.7595028\tbest: 0.7595028 (10)\ttotal: 8.92s\tremaining: 6m 36s\n11:\ttest: 0.7637255\tbest: 0.7637255 (11)\ttotal: 9.62s\tremaining: 6m 31s\n12:\ttest: 0.7591114\tbest: 0.7637255 (11)\ttotal: 10.4s\tremaining: 6m 30s\n13:\ttest: 0.7601125\tbest: 0.7637255 (11)\ttotal: 11.1s\tremaining: 6m 26s\n14:\ttest: 0.7614353\tbest: 0.7637255 (11)\ttotal: 12s\tremaining: 6m 28s\n15:\ttest: 0.7618830\tbest: 0.7637255 (11)\ttotal: 13s\tremaining: 6m 33s\n16:\ttest: 0.7608616\tbest: 0.7637255 (11)\ttotal: 13.8s\tremaining: 6m 32s\n17:\ttest: 0.7598943\tbest: 0.7637255 (11)\ttotal: 14.7s\tremaining: 6m 34s\n18:\ttest: 0.7602452\tbest: 0.7637255 (11)\ttotal: 15.3s\tremaining: 6m 27s\n19:\ttest: 0.7594938\tbest: 0.7637255 (11)\ttotal: 15.9s\tremaining: 6m 22s\n20:\ttest: 0.7583690\tbest: 0.7637255 (11)\ttotal: 16.7s\tremaining: 6m 21s\n21:\ttest: 0.7602857\tbest: 0.7637255 (11)\ttotal: 17.5s\tremaining: 6m 20s\n22:\ttest: 0.7622025\tbest: 0.7637255 (11)\ttotal: 18.3s\tremaining: 6m 19s\n23:\ttest: 0.7627537\tbest: 0.7637255 (11)\ttotal: 19.1s\tremaining: 6m 19s\n24:\ttest: 0.7632936\tbest: 0.7637255 (11)\ttotal: 19.9s\tremaining: 6m 18s\n25:\ttest: 0.7627132\tbest: 0.7637255 (11)\ttotal: 20.7s\tremaining: 6m 17s\n26:\ttest: 0.7623442\tbest: 0.7637255 (11)\ttotal: 21.5s\tremaining: 6m 16s\n27:\ttest: 0.7636175\tbest: 0.7637255 (11)\ttotal: 22.3s\tremaining: 6m 16s\n28:\ttest: 0.7642295\tbest: 0.7642295 (28)\ttotal: 23s\tremaining: 6m 13s\n29:\ttest: 0.7641530\tbest: 0.7642295 (28)\ttotal: 23.9s\tremaining: 6m 14s\n30:\ttest: 0.7642025\tbest: 0.7642295 (28)\ttotal: 24.8s\tremaining: 6m 15s\n31:\ttest: 0.7640090\tbest: 0.7642295 (28)\ttotal: 25.6s\tremaining: 6m 14s\n32:\ttest: 0.7640315\tbest: 0.7642295 (28)\ttotal: 26.5s\tremaining: 6m 15s\n33:\ttest: 0.7650439\tbest: 0.7650439 (33)\ttotal: 27.2s\tremaining: 6m 12s\n34:\ttest: 0.7650259\tbest: 0.7650439 (33)\ttotal: 27.9s\tremaining: 6m 10s\n35:\ttest: 0.7640990\tbest: 0.7650439 (33)\ttotal: 28.7s\tremaining: 6m 9s\n36:\ttest: 0.7647154\tbest: 0.7650439 (33)\ttotal: 29.3s\tremaining: 6m 6s\n37:\ttest: 0.7654308\tbest: 0.7654308 (37)\ttotal: 30s\tremaining: 6m 4s\n38:\ttest: 0.7673071\tbest: 0.7673071 (38)\ttotal: 31s\tremaining: 6m 6s\n39:\ttest: 0.7674871\tbest: 0.7674871 (39)\ttotal: 31.8s\tremaining: 6m 5s\n40:\ttest: 0.7682925\tbest: 0.7682925 (40)\ttotal: 32.6s\tremaining: 6m 5s\n41:\ttest: 0.7687694\tbest: 0.7687694 (41)\ttotal: 33.4s\tremaining: 6m 4s\n42:\ttest: 0.7689809\tbest: 0.7689809 (42)\ttotal: 34.2s\tremaining: 6m 3s\n43:\ttest: 0.7688099\tbest: 0.7689809 (42)\ttotal: 35s\tremaining: 6m 2s\n44:\ttest: 0.7682115\tbest: 0.7689809 (42)\ttotal: 35.7s\tremaining: 6m 1s\n45:\ttest: 0.7679415\tbest: 0.7689809 (42)\ttotal: 36.5s\tremaining: 6m\n46:\ttest: 0.7687514\tbest: 0.7689809 (42)\ttotal: 37.4s\tremaining: 6m\n47:\ttest: 0.7691339\tbest: 0.7691339 (47)\ttotal: 38.1s\tremaining: 5m 58s\n48:\ttest: 0.7706232\tbest: 0.7706232 (48)\ttotal: 38.8s\tremaining: 5m 57s\n49:\ttest: 0.7707987\tbest: 0.7707987 (49)\ttotal: 39.6s\tremaining: 5m 56s\n50:\ttest: 0.7705827\tbest: 0.7707987 (49)\ttotal: 40.4s\tremaining: 5m 55s\n51:\ttest: 0.7710326\tbest: 0.7710326 (51)\ttotal: 41.1s\tremaining: 5m 54s\n52:\ttest: 0.7715276\tbest: 0.7715276 (52)\ttotal: 41.9s\tremaining: 5m 53s\n53:\ttest: 0.7720135\tbest: 0.7720135 (53)\ttotal: 42.7s\tremaining: 5m 52s\n54:\ttest: 0.7714466\tbest: 0.7720135 (53)\ttotal: 43.3s\tremaining: 5m 50s\n55:\ttest: 0.7719460\tbest: 0.7720135 (53)\ttotal: 44.3s\tremaining: 5m 51s\n56:\ttest: 0.7720900\tbest: 0.7720900 (56)\ttotal: 45.2s\tremaining: 5m 51s\n57:\ttest: 0.7726164\tbest: 0.7726164 (57)\ttotal: 46s\tremaining: 5m 50s\n58:\ttest: 0.7725714\tbest: 0.7726164 (57)\ttotal: 46.8s\tremaining: 5m 49s\n59:\ttest: 0.7722970\tbest: 0.7726164 (57)\ttotal: 47.7s\tremaining: 5m 49s\n60:\ttest: 0.7727334\tbest: 0.7727334 (60)\ttotal: 48.4s\tremaining: 5m 48s\n61:\ttest: 0.7730034\tbest: 0.7730034 (61)\ttotal: 49s\tremaining: 5m 46s\n62:\ttest: 0.7730799\tbest: 0.7730799 (62)\ttotal: 49.6s\tremaining: 5m 44s\n63:\ttest: 0.7726209\tbest: 0.7730799 (62)\ttotal: 50.4s\tremaining: 5m 43s\n64:\ttest: 0.7724724\tbest: 0.7730799 (62)\ttotal: 50.9s\tremaining: 5m 40s\n65:\ttest: 0.7715186\tbest: 0.7730799 (62)\ttotal: 51.7s\tremaining: 5m 39s\n66:\ttest: 0.7717480\tbest: 0.7730799 (62)\ttotal: 52.4s\tremaining: 5m 38s\n67:\ttest: 0.7718290\tbest: 0.7730799 (62)\ttotal: 53.2s\tremaining: 5m 38s\n68:\ttest: 0.7724994\tbest: 0.7730799 (62)\ttotal: 54.1s\tremaining: 5m 37s\n69:\ttest: 0.7731699\tbest: 0.7731699 (69)\ttotal: 54.8s\tremaining: 5m 36s\n70:\ttest: 0.7728459\tbest: 0.7731699 (69)\ttotal: 55.5s\tremaining: 5m 35s\n71:\ttest: 0.7732013\tbest: 0.7732013 (71)\ttotal: 56.3s\tremaining: 5m 34s\n72:\ttest: 0.7725354\tbest: 0.7732013 (71)\ttotal: 57.1s\tremaining: 5m 33s\n73:\ttest: 0.7723285\tbest: 0.7732013 (71)\ttotal: 58s\tremaining: 5m 33s\n74:\ttest: 0.7724679\tbest: 0.7732013 (71)\ttotal: 58.9s\tremaining: 5m 33s\n75:\ttest: 0.7720270\tbest: 0.7732013 (71)\ttotal: 59.6s\tremaining: 5m 32s\n76:\ttest: 0.7722115\tbest: 0.7732013 (71)\ttotal: 1m\tremaining: 5m 30s\n77:\ttest: 0.7726749\tbest: 0.7732013 (71)\ttotal: 1m\tremaining: 5m 29s\n78:\ttest: 0.7723780\tbest: 0.7732013 (71)\ttotal: 1m 1s\tremaining: 5m 28s\n79:\ttest: 0.7724229\tbest: 0.7732013 (71)\ttotal: 1m 2s\tremaining: 5m 27s\n80:\ttest: 0.7722430\tbest: 0.7732013 (71)\ttotal: 1m 3s\tremaining: 5m 26s\n81:\ttest: 0.7727829\tbest: 0.7732013 (71)\ttotal: 1m 4s\tremaining: 5m 26s\n82:\ttest: 0.7726164\tbest: 0.7732013 (71)\ttotal: 1m 4s\tremaining: 5m 26s\n83:\ttest: 0.7725399\tbest: 0.7732013 (71)\ttotal: 1m 5s\tremaining: 5m 24s\n84:\ttest: 0.7729539\tbest: 0.7732013 (71)\ttotal: 1m 6s\tremaining: 5m 23s\n85:\ttest: 0.7730079\tbest: 0.7732013 (71)\ttotal: 1m 7s\tremaining: 5m 23s\n86:\ttest: 0.7732958\tbest: 0.7732958 (86)\ttotal: 1m 7s\tremaining: 5m 22s\n87:\ttest: 0.7730079\tbest: 0.7732958 (86)\ttotal: 1m 8s\tremaining: 5m 21s\n88:\ttest: 0.7730034\tbest: 0.7732958 (86)\ttotal: 1m 9s\tremaining: 5m 21s\n89:\ttest: 0.7729134\tbest: 0.7732958 (86)\ttotal: 1m 10s\tremaining: 5m 20s\n90:\ttest: 0.7725219\tbest: 0.7732958 (86)\ttotal: 1m 11s\tremaining: 5m 19s\n91:\ttest: 0.7726614\tbest: 0.7732958 (86)\ttotal: 1m 11s\tremaining: 5m 18s\n92:\ttest: 0.7726254\tbest: 0.7732958 (86)\ttotal: 1m 12s\tremaining: 5m 17s\n93:\ttest: 0.7723375\tbest: 0.7732958 (86)\ttotal: 1m 13s\tremaining: 5m 17s\n94:\ttest: 0.7724454\tbest: 0.7732958 (86)\ttotal: 1m 14s\tremaining: 5m 16s\n95:\ttest: 0.7725669\tbest: 0.7732958 (86)\ttotal: 1m 14s\tremaining: 5m 15s\n96:\ttest: 0.7724409\tbest: 0.7732958 (86)\ttotal: 1m 15s\tremaining: 5m 14s\n97:\ttest: 0.7721395\tbest: 0.7732958 (86)\ttotal: 1m 16s\tremaining: 5m 14s\n98:\ttest: 0.7721035\tbest: 0.7732958 (86)\ttotal: 1m 17s\tremaining: 5m 13s\n99:\ttest: 0.7720000\tbest: 0.7732958 (86)\ttotal: 1m 18s\tremaining: 5m 13s\n100:\ttest: 0.7719820\tbest: 0.7732958 (86)\ttotal: 1m 19s\tremaining: 5m 12s\n101:\ttest: 0.7721395\tbest: 0.7732958 (86)\ttotal: 1m 19s\tremaining: 5m 12s\n102:\ttest: 0.7719595\tbest: 0.7732958 (86)\ttotal: 1m 20s\tremaining: 5m 10s\n103:\ttest: 0.7722295\tbest: 0.7732958 (86)\ttotal: 1m 21s\tremaining: 5m 9s\n104:\ttest: 0.7722610\tbest: 0.7732958 (86)\ttotal: 1m 21s\tremaining: 5m 8s\n105:\ttest: 0.7726344\tbest: 0.7732958 (86)\ttotal: 1m 22s\tremaining: 5m 8s\n106:\ttest: 0.7726479\tbest: 0.7732958 (86)\ttotal: 1m 23s\tremaining: 5m 7s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.773295838\nbestIteration = 86\n\nShrink model to first 87 iterations.\n0:\ttest: 0.6833521\tbest: 0.6833521 (0)\ttotal: 793ms\tremaining: 6m 35s\n1:\ttest: 0.7350349\tbest: 0.7350349 (1)\ttotal: 1.69s\tremaining: 7m 1s\n2:\ttest: 0.8069944\tbest: 0.8069944 (2)\ttotal: 2.49s\tremaining: 6m 52s\n3:\ttest: 0.8059528\tbest: 0.8069944 (2)\ttotal: 3.39s\tremaining: 7m\n4:\ttest: 0.8336963\tbest: 0.8336963 (4)\ttotal: 4.29s\tremaining: 7m 4s\n5:\ttest: 0.8441530\tbest: 0.8441530 (5)\ttotal: 5.08s\tremaining: 6m 58s\n6:\ttest: 0.8433791\tbest: 0.8441530 (5)\ttotal: 5.79s\tremaining: 6m 48s\n7:\ttest: 0.8434556\tbest: 0.8441530 (5)\ttotal: 6.79s\tremaining: 6m 57s\n8:\ttest: 0.8447469\tbest: 0.8447469 (8)\ttotal: 7.59s\tremaining: 6m 53s\n9:\ttest: 0.8447919\tbest: 0.8447919 (9)\ttotal: 8.48s\tremaining: 6m 55s\n10:\ttest: 0.8484612\tbest: 0.8484612 (10)\ttotal: 9.28s\tremaining: 6m 52s\n11:\ttest: 0.8486434\tbest: 0.8486434 (11)\ttotal: 10.2s\tremaining: 6m 54s\n12:\ttest: 0.8475208\tbest: 0.8486434 (11)\ttotal: 10.9s\tremaining: 6m 47s\n13:\ttest: 0.8502137\tbest: 0.8502137 (13)\ttotal: 11.6s\tremaining: 6m 42s\n14:\ttest: 0.8448234\tbest: 0.8502137 (13)\ttotal: 12.4s\tremaining: 6m 40s\n15:\ttest: 0.8489719\tbest: 0.8502137 (13)\ttotal: 13.1s\tremaining: 6m 35s\n16:\ttest: 0.8481845\tbest: 0.8502137 (13)\ttotal: 13.9s\tremaining: 6m 34s\n17:\ttest: 0.8500787\tbest: 0.8502137 (13)\ttotal: 14.7s\tremaining: 6m 33s\n18:\ttest: 0.8519820\tbest: 0.8519820 (18)\ttotal: 15.3s\tremaining: 6m 27s\n19:\ttest: 0.8517300\tbest: 0.8519820 (18)\ttotal: 16.1s\tremaining: 6m 26s\n20:\ttest: 0.8510011\tbest: 0.8519820 (18)\ttotal: 16.9s\tremaining: 6m 25s\n21:\ttest: 0.8454173\tbest: 0.8519820 (18)\ttotal: 17.6s\tremaining: 6m 21s\n22:\ttest: 0.8464747\tbest: 0.8519820 (18)\ttotal: 18.4s\tremaining: 6m 21s\n23:\ttest: 0.8461417\tbest: 0.8519820 (18)\ttotal: 19.3s\tremaining: 6m 22s\n24:\ttest: 0.8447019\tbest: 0.8519820 (18)\ttotal: 20.2s\tremaining: 6m 23s\n25:\ttest: 0.8444004\tbest: 0.8519820 (18)\ttotal: 21.1s\tremaining: 6m 24s\n26:\ttest: 0.8452013\tbest: 0.8519820 (18)\ttotal: 21.6s\tremaining: 6m 17s\n27:\ttest: 0.8449494\tbest: 0.8519820 (18)\ttotal: 22.5s\tremaining: 6m 19s\n28:\ttest: 0.8447649\tbest: 0.8519820 (18)\ttotal: 23.2s\tremaining: 6m 16s\n29:\ttest: 0.8456423\tbest: 0.8519820 (18)\ttotal: 23.8s\tremaining: 6m 12s\n30:\ttest: 0.8457143\tbest: 0.8519820 (18)\ttotal: 24.5s\tremaining: 6m 10s\n31:\ttest: 0.8457683\tbest: 0.8519820 (18)\ttotal: 25.2s\tremaining: 6m 8s\n32:\ttest: 0.8447199\tbest: 0.8519820 (18)\ttotal: 26s\tremaining: 6m 7s\n33:\ttest: 0.8444319\tbest: 0.8519820 (18)\ttotal: 26.7s\tremaining: 6m 5s\n34:\ttest: 0.8444814\tbest: 0.8519820 (18)\ttotal: 27.4s\tremaining: 6m 3s\n35:\ttest: 0.8443060\tbest: 0.8519820 (18)\ttotal: 28.3s\tremaining: 6m 4s\n36:\ttest: 0.8424612\tbest: 0.8519820 (18)\ttotal: 28.9s\tremaining: 6m 1s\n37:\ttest: 0.8433116\tbest: 0.8519820 (18)\ttotal: 29.8s\tremaining: 6m 2s\n38:\ttest: 0.8432216\tbest: 0.8519820 (18)\ttotal: 30.4s\tremaining: 5m 59s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8519820022\nbestIteration = 18\n\nShrink model to first 19 iterations.\n0:\ttest: 0.7929201\tbest: 0.7929201 (0)\ttotal: 798ms\tremaining: 6m 38s\n1:\ttest: 0.8038313\tbest: 0.8038313 (1)\ttotal: 1.6s\tremaining: 6m 39s\n2:\ttest: 0.8193453\tbest: 0.8193453 (2)\ttotal: 2.49s\tremaining: 6m 53s\n3:\ttest: 0.8189944\tbest: 0.8193453 (2)\ttotal: 3.19s\tremaining: 6m 36s\n4:\ttest: 0.8214646\tbest: 0.8214646 (4)\ttotal: 3.91s\tremaining: 6m 27s\n5:\ttest: 0.8204972\tbest: 0.8214646 (4)\ttotal: 4.6s\tremaining: 6m 18s\n6:\ttest: 0.8417548\tbest: 0.8417548 (6)\ttotal: 5.3s\tremaining: 6m 13s\n7:\ttest: 0.8403847\tbest: 0.8417548 (6)\ttotal: 6.09s\tremaining: 6m 14s\n8:\ttest: 0.8460945\tbest: 0.8460945 (8)\ttotal: 6.7s\tremaining: 6m 5s\n9:\ttest: 0.8434961\tbest: 0.8460945 (8)\ttotal: 7.4s\tremaining: 6m 2s\n10:\ttest: 0.8440405\tbest: 0.8460945 (8)\ttotal: 8.29s\tremaining: 6m 8s\n11:\ttest: 0.8463217\tbest: 0.8463217 (11)\ttotal: 8.9s\tremaining: 6m 1s\n12:\ttest: 0.8469651\tbest: 0.8469651 (12)\ttotal: 9.7s\tremaining: 6m 3s\n13:\ttest: 0.8477660\tbest: 0.8477660 (13)\ttotal: 10.6s\tremaining: 6m 7s\n14:\ttest: 0.8494488\tbest: 0.8494488 (14)\ttotal: 11.3s\tremaining: 6m 5s\n15:\ttest: 0.8486119\tbest: 0.8494488 (14)\ttotal: 12.1s\tremaining: 6m 5s\n16:\ttest: 0.8452868\tbest: 0.8494488 (14)\ttotal: 12.8s\tremaining: 6m 3s\n17:\ttest: 0.8452823\tbest: 0.8494488 (14)\ttotal: 13.4s\tremaining: 5m 59s\n18:\ttest: 0.8428301\tbest: 0.8494488 (14)\ttotal: 14.1s\tremaining: 5m 57s\n19:\ttest: 0.8400855\tbest: 0.8494488 (14)\ttotal: 14.9s\tremaining: 5m 57s\n20:\ttest: 0.8398830\tbest: 0.8494488 (14)\ttotal: 15.6s\tremaining: 5m 56s\n21:\ttest: 0.8417008\tbest: 0.8494488 (14)\ttotal: 16.1s\tremaining: 5m 50s\n22:\ttest: 0.8399595\tbest: 0.8494488 (14)\ttotal: 16.9s\tremaining: 5m 50s\n23:\ttest: 0.8418583\tbest: 0.8494488 (14)\ttotal: 17.5s\tremaining: 5m 47s\n24:\ttest: 0.8419483\tbest: 0.8494488 (14)\ttotal: 18.2s\tremaining: 5m 46s\n25:\ttest: 0.8424297\tbest: 0.8494488 (14)\ttotal: 18.9s\tremaining: 5m 44s\n26:\ttest: 0.8432531\tbest: 0.8494488 (14)\ttotal: 19.7s\tremaining: 5m 45s\n27:\ttest: 0.8433341\tbest: 0.8494488 (14)\ttotal: 20.6s\tremaining: 5m 47s\n28:\ttest: 0.8437030\tbest: 0.8494488 (14)\ttotal: 21.2s\tremaining: 5m 44s\n29:\ttest: 0.8440900\tbest: 0.8494488 (14)\ttotal: 22s\tremaining: 5m 44s\n30:\ttest: 0.8411069\tbest: 0.8494488 (14)\ttotal: 22.6s\tremaining: 5m 42s\n31:\ttest: 0.8409899\tbest: 0.8494488 (14)\ttotal: 23.2s\tremaining: 5m 39s\n32:\ttest: 0.8413813\tbest: 0.8494488 (14)\ttotal: 24s\tremaining: 5m 40s\n33:\ttest: 0.8418538\tbest: 0.8494488 (14)\ttotal: 24.9s\tremaining: 5m 41s\n34:\ttest: 0.8432126\tbest: 0.8494488 (14)\ttotal: 25.5s\tremaining: 5m 39s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8494488189\nbestIteration = 14\n\nShrink model to first 15 iterations.\n0:\ttest: 0.6514488\tbest: 0.6514488 (0)\ttotal: 788ms\tremaining: 6m 33s\n1:\ttest: 0.6699775\tbest: 0.6699775 (1)\ttotal: 1.58s\tremaining: 6m 34s\n2:\ttest: 0.6677503\tbest: 0.6699775 (1)\ttotal: 2.18s\tremaining: 6m 1s\n3:\ttest: 0.6695838\tbest: 0.6699775 (1)\ttotal: 2.9s\tremaining: 5m 59s\n4:\ttest: 0.7303712\tbest: 0.7303712 (4)\ttotal: 3.89s\tremaining: 6m 25s\n5:\ttest: 0.7304522\tbest: 0.7304522 (5)\ttotal: 4.4s\tremaining: 6m 1s\n6:\ttest: 0.7487222\tbest: 0.7487222 (6)\ttotal: 5.19s\tremaining: 6m 5s\n7:\ttest: 0.7531519\tbest: 0.7531519 (7)\ttotal: 5.99s\tremaining: 6m 8s\n8:\ttest: 0.7510281\tbest: 0.7531519 (7)\ttotal: 6.6s\tremaining: 5m 59s\n9:\ttest: 0.7556760\tbest: 0.7556760 (9)\ttotal: 7.28s\tremaining: 5m 56s\n10:\ttest: 0.7558200\tbest: 0.7558200 (10)\ttotal: 7.9s\tremaining: 5m 51s\n11:\ttest: 0.7533048\tbest: 0.7558200 (10)\ttotal: 8.69s\tremaining: 5m 53s\n12:\ttest: 0.7542880\tbest: 0.7558200 (10)\ttotal: 9.48s\tremaining: 5m 55s\n13:\ttest: 0.7559685\tbest: 0.7559685 (13)\ttotal: 10.1s\tremaining: 5m 50s\n14:\ttest: 0.7557728\tbest: 0.7559685 (13)\ttotal: 11s\tremaining: 5m 55s\n15:\ttest: 0.7541260\tbest: 0.7559685 (13)\ttotal: 11.7s\tremaining: 5m 53s\n16:\ttest: 0.7568301\tbest: 0.7568301 (16)\ttotal: 12.4s\tremaining: 5m 51s\n17:\ttest: 0.7571969\tbest: 0.7571969 (17)\ttotal: 13.1s\tremaining: 5m 50s\n18:\ttest: 0.7590236\tbest: 0.7590236 (18)\ttotal: 13.9s\tremaining: 5m 51s\n19:\ttest: 0.7588256\tbest: 0.7590236 (18)\ttotal: 14.4s\tremaining: 5m 45s\n20:\ttest: 0.7580382\tbest: 0.7590236 (18)\ttotal: 15.2s\tremaining: 5m 46s\n21:\ttest: 0.7598830\tbest: 0.7598830 (21)\ttotal: 16s\tremaining: 5m 47s\n22:\ttest: 0.7614758\tbest: 0.7614758 (22)\ttotal: 16.9s\tremaining: 5m 49s\n23:\ttest: 0.7621327\tbest: 0.7621327 (23)\ttotal: 17.6s\tremaining: 5m 48s\n24:\ttest: 0.7637345\tbest: 0.7637345 (24)\ttotal: 18.3s\tremaining: 5m 47s\n25:\ttest: 0.7626277\tbest: 0.7637345 (24)\ttotal: 19s\tremaining: 5m 46s\n26:\ttest: 0.7641350\tbest: 0.7641350 (26)\ttotal: 19.7s\tremaining: 5m 44s\n27:\ttest: 0.7648324\tbest: 0.7648324 (27)\ttotal: 20.3s\tremaining: 5m 41s\n28:\ttest: 0.7633566\tbest: 0.7648324 (27)\ttotal: 21s\tremaining: 5m 40s\n29:\ttest: 0.7628391\tbest: 0.7648324 (27)\ttotal: 21.9s\tremaining: 5m 42s\n30:\ttest: 0.7644724\tbest: 0.7648324 (27)\ttotal: 22.6s\tremaining: 5m 41s\n31:\ttest: 0.7631361\tbest: 0.7648324 (27)\ttotal: 23.4s\tremaining: 5m 41s\n32:\ttest: 0.7642475\tbest: 0.7648324 (27)\ttotal: 24s\tremaining: 5m 39s\n33:\ttest: 0.7656603\tbest: 0.7656603 (33)\ttotal: 25s\tremaining: 5m 42s\n34:\ttest: 0.7672171\tbest: 0.7672171 (34)\ttotal: 25.6s\tremaining: 5m 39s\n35:\ttest: 0.7671811\tbest: 0.7672171 (34)\ttotal: 26.6s\tremaining: 5m 42s\n36:\ttest: 0.7662767\tbest: 0.7672171 (34)\ttotal: 27.3s\tremaining: 5m 41s\n37:\ttest: 0.7654893\tbest: 0.7672171 (34)\ttotal: 28.2s\tremaining: 5m 42s\n38:\ttest: 0.7643690\tbest: 0.7672171 (34)\ttotal: 28.9s\tremaining: 5m 41s\n39:\ttest: 0.7652823\tbest: 0.7672171 (34)\ttotal: 29.7s\tremaining: 5m 41s\n40:\ttest: 0.7645174\tbest: 0.7672171 (34)\ttotal: 30.6s\tremaining: 5m 42s\n41:\ttest: 0.7642160\tbest: 0.7672171 (34)\ttotal: 31.3s\tremaining: 5m 40s\n42:\ttest: 0.7640045\tbest: 0.7672171 (34)\ttotal: 32.2s\tremaining: 5m 41s\n43:\ttest: 0.7626457\tbest: 0.7672171 (34)\ttotal: 32.8s\tremaining: 5m 39s\n44:\ttest: 0.7629111\tbest: 0.7672171 (34)\ttotal: 33.8s\tremaining: 5m 41s\n45:\ttest: 0.7628841\tbest: 0.7672171 (34)\ttotal: 34.6s\tremaining: 5m 41s\n46:\ttest: 0.7627852\tbest: 0.7672171 (34)\ttotal: 35.3s\tremaining: 5m 39s\n47:\ttest: 0.7627087\tbest: 0.7672171 (34)\ttotal: 36.2s\tremaining: 5m 40s\n48:\ttest: 0.7633116\tbest: 0.7672171 (34)\ttotal: 37s\tremaining: 5m 40s\n49:\ttest: 0.7635186\tbest: 0.7672171 (34)\ttotal: 37.7s\tremaining: 5m 38s\n50:\ttest: 0.7634871\tbest: 0.7672171 (34)\ttotal: 38.4s\tremaining: 5m 37s\n51:\ttest: 0.7633251\tbest: 0.7672171 (34)\ttotal: 39.2s\tremaining: 5m 37s\n52:\ttest: 0.7641665\tbest: 0.7672171 (34)\ttotal: 39.9s\tremaining: 5m 36s\n53:\ttest: 0.7633386\tbest: 0.7672171 (34)\ttotal: 40.7s\tremaining: 5m 35s\n54:\ttest: 0.7634466\tbest: 0.7672171 (34)\ttotal: 41.5s\tremaining: 5m 35s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7672170979\nbestIteration = 34\n\nShrink model to first 35 iterations.\n0:\ttest: 0.6833521\tbest: 0.6833521 (0)\ttotal: 697ms\tremaining: 5m 47s\n1:\ttest: 0.7350889\tbest: 0.7350889 (1)\ttotal: 1.39s\tremaining: 5m 45s\n2:\ttest: 0.8069449\tbest: 0.8069449 (2)\ttotal: 1.99s\tremaining: 5m 30s\n3:\ttest: 0.8060337\tbest: 0.8069449 (2)\ttotal: 2.6s\tremaining: 5m 22s\n4:\ttest: 0.8495096\tbest: 0.8495096 (4)\ttotal: 3.3s\tremaining: 5m 26s\n5:\ttest: 0.8576310\tbest: 0.8576310 (5)\ttotal: 4s\tremaining: 5m 29s\n6:\ttest: 0.8581215\tbest: 0.8581215 (6)\ttotal: 4.89s\tremaining: 5m 44s\n7:\ttest: 0.8598155\tbest: 0.8598155 (7)\ttotal: 5.58s\tremaining: 5m 43s\n8:\ttest: 0.8547649\tbest: 0.8598155 (7)\ttotal: 6.29s\tremaining: 5m 43s\n9:\ttest: 0.8527492\tbest: 0.8598155 (7)\ttotal: 6.99s\tremaining: 5m 42s\n10:\ttest: 0.8538605\tbest: 0.8598155 (7)\ttotal: 7.78s\tremaining: 5m 46s\n11:\ttest: 0.8533093\tbest: 0.8598155 (7)\ttotal: 8.59s\tremaining: 5m 49s\n12:\ttest: 0.8532643\tbest: 0.8598155 (7)\ttotal: 9.49s\tremaining: 5m 55s\n13:\ttest: 0.8542092\tbest: 0.8598155 (7)\ttotal: 10.3s\tremaining: 5m 57s\n14:\ttest: 0.8527109\tbest: 0.8598155 (7)\ttotal: 10.9s\tremaining: 5m 52s\n15:\ttest: 0.8493408\tbest: 0.8598155 (7)\ttotal: 11.6s\tremaining: 5m 50s\n16:\ttest: 0.8483510\tbest: 0.8598155 (7)\ttotal: 12.4s\tremaining: 5m 51s\n17:\ttest: 0.8395006\tbest: 0.8598155 (7)\ttotal: 13.1s\tremaining: 5m 50s\n18:\ttest: 0.8388661\tbest: 0.8598155 (7)\ttotal: 13.7s\tremaining: 5m 46s\n19:\ttest: 0.8392036\tbest: 0.8598155 (7)\ttotal: 14.6s\tremaining: 5m 50s\n20:\ttest: 0.8358290\tbest: 0.8598155 (7)\ttotal: 15.2s\tremaining: 5m 46s\n21:\ttest: 0.8363015\tbest: 0.8598155 (7)\ttotal: 15.9s\tremaining: 5m 45s\n22:\ttest: 0.8367919\tbest: 0.8598155 (7)\ttotal: 16.6s\tremaining: 5m 43s\n23:\ttest: 0.8334173\tbest: 0.8598155 (7)\ttotal: 17.2s\tremaining: 5m 40s\n24:\ttest: 0.8315816\tbest: 0.8598155 (7)\ttotal: 17.8s\tremaining: 5m 38s\n25:\ttest: 0.8332103\tbest: 0.8598155 (7)\ttotal: 18.7s\tremaining: 5m 40s\n26:\ttest: 0.8336108\tbest: 0.8598155 (7)\ttotal: 19.4s\tremaining: 5m 39s\n27:\ttest: 0.8345647\tbest: 0.8598155 (7)\ttotal: 20.1s\tremaining: 5m 38s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8598155231\nbestIteration = 7\n\nShrink model to first 8 iterations.\n0:\ttest: 0.7929201\tbest: 0.7929201 (0)\ttotal: 791ms\tremaining: 6m 34s\n1:\ttest: 0.8049831\tbest: 0.8049831 (1)\ttotal: 1.58s\tremaining: 6m 34s\n2:\ttest: 0.8207537\tbest: 0.8207537 (2)\ttotal: 2.39s\tremaining: 6m 36s\n3:\ttest: 0.8200877\tbest: 0.8207537 (2)\ttotal: 3.2s\tremaining: 6m 36s\n4:\ttest: 0.8240675\tbest: 0.8240675 (4)\ttotal: 3.9s\tremaining: 6m 25s\n5:\ttest: 0.8248796\tbest: 0.8248796 (5)\ttotal: 4.89s\tremaining: 6m 42s\n6:\ttest: 0.8326952\tbest: 0.8326952 (6)\ttotal: 5.5s\tremaining: 6m 27s\n7:\ttest: 0.8314443\tbest: 0.8326952 (6)\ttotal: 6.1s\tremaining: 6m 15s\n8:\ttest: 0.8351856\tbest: 0.8351856 (8)\ttotal: 6.79s\tremaining: 6m 10s\n9:\ttest: 0.8348796\tbest: 0.8351856 (8)\ttotal: 7.49s\tremaining: 6m 6s\n10:\ttest: 0.8294038\tbest: 0.8351856 (8)\ttotal: 8.09s\tremaining: 5m 59s\n11:\ttest: 0.8276895\tbest: 0.8351856 (8)\ttotal: 8.89s\tremaining: 6m 1s\n12:\ttest: 0.8299168\tbest: 0.8351856 (8)\ttotal: 9.59s\tremaining: 5m 59s\n13:\ttest: 0.8275951\tbest: 0.8351856 (8)\ttotal: 10.4s\tremaining: 6m\n14:\ttest: 0.8309921\tbest: 0.8351856 (8)\ttotal: 11.1s\tremaining: 5m 58s\n15:\ttest: 0.8307897\tbest: 0.8351856 (8)\ttotal: 11.8s\tremaining: 5m 57s\n16:\ttest: 0.8316085\tbest: 0.8351856 (8)\ttotal: 12.6s\tremaining: 5m 58s\n17:\ttest: 0.8306187\tbest: 0.8351856 (8)\ttotal: 13.3s\tremaining: 5m 56s\n18:\ttest: 0.8322925\tbest: 0.8351856 (8)\ttotal: 14.1s\tremaining: 5m 57s\n19:\ttest: 0.8325984\tbest: 0.8351856 (8)\ttotal: 14.9s\tremaining: 5m 57s\n20:\ttest: 0.8322925\tbest: 0.8351856 (8)\ttotal: 15.5s\tremaining: 5m 53s\n21:\ttest: 0.8330259\tbest: 0.8351856 (8)\ttotal: 16.5s\tremaining: 5m 58s\n22:\ttest: 0.8337323\tbest: 0.8351856 (8)\ttotal: 17.3s\tremaining: 5m 58s\n23:\ttest: 0.8353971\tbest: 0.8353971 (23)\ttotal: 18.1s\tremaining: 5m 59s\n24:\ttest: 0.8341057\tbest: 0.8353971 (23)\ttotal: 18.8s\tremaining: 5m 57s\n25:\ttest: 0.8351451\tbest: 0.8353971 (23)\ttotal: 19.3s\tremaining: 5m 52s\n26:\ttest: 0.8347807\tbest: 0.8353971 (23)\ttotal: 20.1s\tremaining: 5m 52s\n27:\ttest: 0.8229786\tbest: 0.8353971 (23)\ttotal: 21s\tremaining: 5m 54s\n28:\ttest: 0.8227492\tbest: 0.8353971 (23)\ttotal: 21.6s\tremaining: 5m 50s\n29:\ttest: 0.8225602\tbest: 0.8353971 (23)\ttotal: 22.2s\tremaining: 5m 47s\n30:\ttest: 0.8229156\tbest: 0.8353971 (23)\ttotal: 22.9s\tremaining: 5m 46s\n31:\ttest: 0.8233341\tbest: 0.8353971 (23)\ttotal: 23.6s\tremaining: 5m 45s\n32:\ttest: 0.8229831\tbest: 0.8353971 (23)\ttotal: 24.3s\tremaining: 5m 44s\n33:\ttest: 0.8226322\tbest: 0.8353971 (23)\ttotal: 25.1s\tremaining: 5m 44s\n34:\ttest: 0.8224027\tbest: 0.8353971 (23)\ttotal: 25.7s\tremaining: 5m 41s\n35:\ttest: 0.8233341\tbest: 0.8353971 (23)\ttotal: 26.3s\tremaining: 5m 38s\n36:\ttest: 0.8238515\tbest: 0.8353971 (23)\ttotal: 27.1s\tremaining: 5m 39s\n37:\ttest: 0.8218898\tbest: 0.8353971 (23)\ttotal: 28s\tremaining: 5m 40s\n38:\ttest: 0.8214533\tbest: 0.8353971 (23)\ttotal: 28.7s\tremaining: 5m 39s\n39:\ttest: 0.8207604\tbest: 0.8353971 (23)\ttotal: 29.5s\tremaining: 5m 39s\n40:\ttest: 0.8204499\tbest: 0.8353971 (23)\ttotal: 30.2s\tremaining: 5m 38s\n41:\ttest: 0.8210709\tbest: 0.8353971 (23)\ttotal: 31.1s\tremaining: 5m 39s\n42:\ttest: 0.8196400\tbest: 0.8353971 (23)\ttotal: 31.7s\tremaining: 5m 36s\n43:\ttest: 0.8199235\tbest: 0.8353971 (23)\ttotal: 32.5s\tremaining: 5m 36s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8353970754\nbestIteration = 23\n\nShrink model to first 24 iterations.\n0:\ttest: 0.6545534\tbest: 0.6545534 (0)\ttotal: 1.09s\tremaining: 9m 2s\n1:\ttest: 0.6774151\tbest: 0.6774151 (1)\ttotal: 1.79s\tremaining: 7m 27s\n2:\ttest: 0.6919460\tbest: 0.6919460 (2)\ttotal: 2.5s\tremaining: 6m 53s\n3:\ttest: 0.6944117\tbest: 0.6944117 (3)\ttotal: 3.28s\tremaining: 6m 46s\n4:\ttest: 0.7552261\tbest: 0.7552261 (4)\ttotal: 3.99s\tremaining: 6m 35s\n5:\ttest: 0.7711586\tbest: 0.7711586 (5)\ttotal: 4.79s\tremaining: 6m 34s\n6:\ttest: 0.7709089\tbest: 0.7711586 (5)\ttotal: 5.49s\tremaining: 6m 26s\n7:\ttest: 0.7714713\tbest: 0.7714713 (7)\ttotal: 6.39s\tremaining: 6m 32s\n8:\ttest: 0.7654443\tbest: 0.7714713 (7)\ttotal: 7.29s\tremaining: 6m 37s\n9:\ttest: 0.7625714\tbest: 0.7714713 (7)\ttotal: 8.09s\tremaining: 6m 36s\n10:\ttest: 0.7611159\tbest: 0.7714713 (7)\ttotal: 8.89s\tremaining: 6m 35s\n11:\ttest: 0.7636108\tbest: 0.7714713 (7)\ttotal: 9.78s\tremaining: 6m 37s\n12:\ttest: 0.7640337\tbest: 0.7714713 (7)\ttotal: 10.4s\tremaining: 6m 29s\n13:\ttest: 0.7642137\tbest: 0.7714713 (7)\ttotal: 11s\tremaining: 6m 21s\n14:\ttest: 0.7715186\tbest: 0.7715186 (14)\ttotal: 11.7s\tremaining: 6m 18s\n15:\ttest: 0.7692913\tbest: 0.7715186 (14)\ttotal: 12.6s\tremaining: 6m 20s\n16:\ttest: 0.7649719\tbest: 0.7715186 (14)\ttotal: 13.4s\tremaining: 6m 20s\n17:\ttest: 0.7648999\tbest: 0.7715186 (14)\ttotal: 14.3s\tremaining: 6m 22s\n18:\ttest: 0.7663532\tbest: 0.7715186 (14)\ttotal: 15.1s\tremaining: 6m 21s\n19:\ttest: 0.7643285\tbest: 0.7715186 (14)\ttotal: 15.8s\tremaining: 6m 19s\n20:\ttest: 0.7642790\tbest: 0.7715186 (14)\ttotal: 16.7s\tremaining: 6m 20s\n21:\ttest: 0.7640630\tbest: 0.7715186 (14)\ttotal: 17.4s\tremaining: 6m 17s\n22:\ttest: 0.7631946\tbest: 0.7715186 (14)\ttotal: 18.2s\tremaining: 6m 17s\n23:\ttest: 0.7648999\tbest: 0.7715186 (14)\ttotal: 18.8s\tremaining: 6m 12s\n24:\ttest: 0.7655928\tbest: 0.7715186 (14)\ttotal: 19.7s\tremaining: 6m 13s\n25:\ttest: 0.7671991\tbest: 0.7715186 (14)\ttotal: 20.4s\tremaining: 6m 11s\n26:\ttest: 0.7683015\tbest: 0.7715186 (14)\ttotal: 21.1s\tremaining: 6m 9s\n27:\ttest: 0.7677975\tbest: 0.7715186 (14)\ttotal: 21.6s\tremaining: 6m 3s\n28:\ttest: 0.7692373\tbest: 0.7715186 (14)\ttotal: 22.3s\tremaining: 6m 1s\n29:\ttest: 0.7695883\tbest: 0.7715186 (14)\ttotal: 23.2s\tremaining: 6m 3s\n30:\ttest: 0.7697323\tbest: 0.7715186 (14)\ttotal: 24s\tremaining: 6m 2s\n31:\ttest: 0.7677435\tbest: 0.7715186 (14)\ttotal: 24.8s\tremaining: 6m 2s\n32:\ttest: 0.7671991\tbest: 0.7715186 (14)\ttotal: 25.7s\tremaining: 6m 3s\n33:\ttest: 0.7657413\tbest: 0.7715186 (14)\ttotal: 26.6s\tremaining: 6m 4s\n34:\ttest: 0.7663172\tbest: 0.7715186 (14)\ttotal: 27.3s\tremaining: 6m 2s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7715185602\nbestIteration = 14\n\nShrink model to first 15 iterations.\n0:\ttest: 0.6830776\tbest: 0.6830776 (0)\ttotal: 697ms\tremaining: 5m 47s\n1:\ttest: 0.7142992\tbest: 0.7142992 (1)\ttotal: 1.4s\tremaining: 5m 47s\n2:\ttest: 0.7913813\tbest: 0.7913813 (2)\ttotal: 2.19s\tremaining: 6m 3s\n3:\ttest: 0.7901665\tbest: 0.7913813 (2)\ttotal: 3.09s\tremaining: 6m 23s\n4:\ttest: 0.8288886\tbest: 0.8288886 (4)\ttotal: 3.9s\tremaining: 6m 25s\n5:\ttest: 0.8372351\tbest: 0.8372351 (5)\ttotal: 4.59s\tremaining: 6m 17s\n6:\ttest: 0.8321485\tbest: 0.8372351 (5)\ttotal: 5.29s\tremaining: 6m 12s\n7:\ttest: 0.8364297\tbest: 0.8372351 (5)\ttotal: 6s\tremaining: 6m 8s\n8:\ttest: 0.8391969\tbest: 0.8391969 (8)\ttotal: 6.89s\tremaining: 6m 15s\n9:\ttest: 0.8392733\tbest: 0.8392733 (9)\ttotal: 7.59s\tremaining: 6m 11s\n10:\ttest: 0.8471001\tbest: 0.8471001 (10)\ttotal: 8.39s\tremaining: 6m 12s\n11:\ttest: 0.8450439\tbest: 0.8471001 (10)\ttotal: 9s\tremaining: 6m 5s\n12:\ttest: 0.8446164\tbest: 0.8471001 (10)\ttotal: 9.88s\tremaining: 6m 10s\n13:\ttest: 0.8437975\tbest: 0.8471001 (10)\ttotal: 10.5s\tremaining: 6m 4s\n14:\ttest: 0.8430821\tbest: 0.8471001 (10)\ttotal: 11.2s\tremaining: 6m 1s\n15:\ttest: 0.8411204\tbest: 0.8471001 (10)\ttotal: 11.8s\tremaining: 5m 56s\n16:\ttest: 0.8438560\tbest: 0.8471001 (10)\ttotal: 12.5s\tremaining: 5m 54s\n17:\ttest: 0.8507132\tbest: 0.8507132 (17)\ttotal: 13.4s\tremaining: 5m 58s\n18:\ttest: 0.8481755\tbest: 0.8507132 (17)\ttotal: 14.1s\tremaining: 5m 56s\n19:\ttest: 0.8431811\tbest: 0.8507132 (17)\ttotal: 14.8s\tremaining: 5m 54s\n20:\ttest: 0.8439055\tbest: 0.8507132 (17)\ttotal: 15.6s\tremaining: 5m 55s\n21:\ttest: 0.8420292\tbest: 0.8507132 (17)\ttotal: 16.4s\tremaining: 5m 55s\n22:\ttest: 0.8460247\tbest: 0.8507132 (17)\ttotal: 17s\tremaining: 5m 52s\n23:\ttest: 0.8459123\tbest: 0.8507132 (17)\ttotal: 17.7s\tremaining: 5m 50s\n24:\ttest: 0.8452913\tbest: 0.8507132 (17)\ttotal: 18.7s\tremaining: 5m 54s\n25:\ttest: 0.8483645\tbest: 0.8507132 (17)\ttotal: 19.4s\tremaining: 5m 53s\n26:\ttest: 0.8493903\tbest: 0.8507132 (17)\ttotal: 20.3s\tremaining: 5m 55s\n27:\ttest: 0.8489224\tbest: 0.8507132 (17)\ttotal: 20.7s\tremaining: 5m 48s\n28:\ttest: 0.8474871\tbest: 0.8507132 (17)\ttotal: 21.4s\tremaining: 5m 47s\n29:\ttest: 0.8450529\tbest: 0.8507132 (17)\ttotal: 22s\tremaining: 5m 44s\n30:\ttest: 0.8443195\tbest: 0.8507132 (17)\ttotal: 22.7s\tremaining: 5m 43s\n31:\ttest: 0.8442880\tbest: 0.8507132 (17)\ttotal: 23.5s\tremaining: 5m 43s\n32:\ttest: 0.8450484\tbest: 0.8507132 (17)\ttotal: 24.3s\tremaining: 5m 43s\n33:\ttest: 0.8450484\tbest: 0.8507132 (17)\ttotal: 25.1s\tremaining: 5m 43s\n34:\ttest: 0.8444184\tbest: 0.8507132 (17)\ttotal: 25.7s\tremaining: 5m 41s\n35:\ttest: 0.8444679\tbest: 0.8507132 (17)\ttotal: 26.4s\tremaining: 5m 40s\n36:\ttest: 0.8458538\tbest: 0.8507132 (17)\ttotal: 27.2s\tremaining: 5m 40s\n37:\ttest: 0.8455613\tbest: 0.8507132 (17)\ttotal: 28s\tremaining: 5m 40s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8507131609\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 720ms\tremaining: 5m 59s\n1:\ttest: 0.7202745\tbest: 0.7202745 (1)\ttotal: 1.6s\tremaining: 6m 39s\n2:\ttest: 0.7755276\tbest: 0.7755276 (2)\ttotal: 2.4s\tremaining: 6m 38s\n3:\ttest: 0.7764859\tbest: 0.7764859 (3)\ttotal: 3.21s\tremaining: 6m 37s\n4:\ttest: 0.7838425\tbest: 0.7838425 (4)\ttotal: 3.91s\tremaining: 6m 26s\n5:\ttest: 0.7810416\tbest: 0.7838425 (4)\ttotal: 4.62s\tremaining: 6m 20s\n6:\ttest: 0.8256175\tbest: 0.8256175 (6)\ttotal: 5.43s\tremaining: 6m 22s\n7:\ttest: 0.8295501\tbest: 0.8295501 (7)\ttotal: 6.23s\tremaining: 6m 23s\n8:\ttest: 0.8414713\tbest: 0.8414713 (8)\ttotal: 7.03s\tremaining: 6m 23s\n9:\ttest: 0.8436828\tbest: 0.8436828 (9)\ttotal: 8.03s\tremaining: 6m 33s\n10:\ttest: 0.8415073\tbest: 0.8436828 (9)\ttotal: 8.92s\tremaining: 6m 36s\n11:\ttest: 0.8450034\tbest: 0.8450034 (11)\ttotal: 9.63s\tremaining: 6m 31s\n12:\ttest: 0.8506839\tbest: 0.8506839 (12)\ttotal: 10.5s\tremaining: 6m 34s\n13:\ttest: 0.8512058\tbest: 0.8512058 (13)\ttotal: 11.2s\tremaining: 6m 29s\n14:\ttest: 0.8529854\tbest: 0.8529854 (14)\ttotal: 11.9s\tremaining: 6m 25s\n15:\ttest: 0.8535613\tbest: 0.8535613 (15)\ttotal: 12.9s\tremaining: 6m 30s\n16:\ttest: 0.8522340\tbest: 0.8535613 (15)\ttotal: 13.8s\tremaining: 6m 32s\n17:\ttest: 0.8535298\tbest: 0.8535613 (15)\ttotal: 14.8s\tremaining: 6m 36s\n18:\ttest: 0.8500967\tbest: 0.8535613 (15)\ttotal: 15.5s\tremaining: 6m 33s\n19:\ttest: 0.8498673\tbest: 0.8535613 (15)\ttotal: 16.3s\tremaining: 6m 31s\n20:\ttest: 0.8490754\tbest: 0.8535613 (15)\ttotal: 17.2s\tremaining: 6m 33s\n21:\ttest: 0.8490844\tbest: 0.8535613 (15)\ttotal: 18s\tremaining: 6m 31s\n22:\ttest: 0.8482160\tbest: 0.8535613 (15)\ttotal: 18.9s\tremaining: 6m 32s\n23:\ttest: 0.8488729\tbest: 0.8535613 (15)\ttotal: 19.5s\tremaining: 6m 27s\n24:\ttest: 0.8481620\tbest: 0.8535613 (15)\ttotal: 20.3s\tremaining: 6m 26s\n25:\ttest: 0.8478425\tbest: 0.8535613 (15)\ttotal: 21.3s\tremaining: 6m 28s\n26:\ttest: 0.8471721\tbest: 0.8535613 (15)\ttotal: 22.1s\tremaining: 6m 27s\n27:\ttest: 0.8472081\tbest: 0.8535613 (15)\ttotal: 23s\tremaining: 6m 28s\n28:\ttest: 0.8485534\tbest: 0.8535613 (15)\ttotal: 23.9s\tremaining: 6m 28s\n29:\ttest: 0.8459483\tbest: 0.8535613 (15)\ttotal: 24.7s\tremaining: 6m 27s\n30:\ttest: 0.8461012\tbest: 0.8535613 (15)\ttotal: 25.7s\tremaining: 6m 29s\n31:\ttest: 0.8460337\tbest: 0.8535613 (15)\ttotal: 26.4s\tremaining: 6m 26s\n32:\ttest: 0.8467447\tbest: 0.8535613 (15)\ttotal: 27.1s\tremaining: 6m 23s\n33:\ttest: 0.8454128\tbest: 0.8535613 (15)\ttotal: 27.7s\tremaining: 6m 19s\n34:\ttest: 0.8456738\tbest: 0.8535613 (15)\ttotal: 28.4s\tremaining: 6m 17s\n35:\ttest: 0.8448459\tbest: 0.8535613 (15)\ttotal: 29.2s\tremaining: 6m 16s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8535613048\nbestIteration = 15\n\nShrink model to first 16 iterations.\n0:\ttest: 0.6545534\tbest: 0.6545534 (0)\ttotal: 804ms\tremaining: 6m 41s\n1:\ttest: 0.6776220\tbest: 0.6776220 (1)\ttotal: 1.6s\tremaining: 6m 39s\n2:\ttest: 0.6911676\tbest: 0.6911676 (2)\ttotal: 2.3s\tremaining: 6m 21s\n3:\ttest: 0.6870641\tbest: 0.6911676 (2)\ttotal: 3.01s\tremaining: 6m 12s\n4:\ttest: 0.7301282\tbest: 0.7301282 (4)\ttotal: 3.79s\tremaining: 6m 15s\n5:\ttest: 0.7552958\tbest: 0.7552958 (5)\ttotal: 4.5s\tremaining: 6m 10s\n6:\ttest: 0.7544387\tbest: 0.7552958 (5)\ttotal: 5.29s\tremaining: 6m 12s\n7:\ttest: 0.7555163\tbest: 0.7555163 (7)\ttotal: 5.89s\tremaining: 6m 2s\n8:\ttest: 0.7561822\tbest: 0.7561822 (8)\ttotal: 6.69s\tremaining: 6m 5s\n9:\ttest: 0.7529336\tbest: 0.7561822 (8)\ttotal: 7.4s\tremaining: 6m 2s\n10:\ttest: 0.7537885\tbest: 0.7561822 (8)\ttotal: 8.09s\tremaining: 5m 59s\n11:\ttest: 0.7565287\tbest: 0.7565287 (11)\ttotal: 8.7s\tremaining: 5m 53s\n12:\ttest: 0.7550484\tbest: 0.7565287 (11)\ttotal: 9.49s\tremaining: 5m 55s\n13:\ttest: 0.7536513\tbest: 0.7565287 (11)\ttotal: 10.2s\tremaining: 5m 53s\n14:\ttest: 0.7563217\tbest: 0.7565287 (11)\ttotal: 10.9s\tremaining: 5m 52s\n15:\ttest: 0.7578583\tbest: 0.7578583 (15)\ttotal: 11.6s\tremaining: 5m 50s\n16:\ttest: 0.7569494\tbest: 0.7578583 (15)\ttotal: 12.5s\tremaining: 5m 54s\n17:\ttest: 0.7566974\tbest: 0.7578583 (15)\ttotal: 13.3s\tremaining: 5m 55s\n18:\ttest: 0.7567649\tbest: 0.7578583 (15)\ttotal: 14.2s\tremaining: 5m 59s\n19:\ttest: 0.7559370\tbest: 0.7578583 (15)\ttotal: 15s\tremaining: 5m 59s\n20:\ttest: 0.7563735\tbest: 0.7578583 (15)\ttotal: 15.7s\tremaining: 5m 57s\n21:\ttest: 0.7551586\tbest: 0.7578583 (15)\ttotal: 16.4s\tremaining: 5m 56s\n22:\ttest: 0.7581147\tbest: 0.7581147 (22)\ttotal: 17.1s\tremaining: 5m 54s\n23:\ttest: 0.7580067\tbest: 0.7581147 (22)\ttotal: 17.9s\tremaining: 5m 54s\n24:\ttest: 0.7586097\tbest: 0.7586097 (24)\ttotal: 18.7s\tremaining: 5m 54s\n25:\ttest: 0.7596333\tbest: 0.7596333 (25)\ttotal: 19.3s\tremaining: 5m 51s\n26:\ttest: 0.7591879\tbest: 0.7596333 (25)\ttotal: 20.1s\tremaining: 5m 51s\n27:\ttest: 0.7591586\tbest: 0.7596333 (25)\ttotal: 20.8s\tremaining: 5m 50s\n28:\ttest: 0.7594151\tbest: 0.7596333 (25)\ttotal: 21.6s\tremaining: 5m 50s\n29:\ttest: 0.7601800\tbest: 0.7601800 (29)\ttotal: 22.4s\tremaining: 5m 50s\n30:\ttest: 0.7610664\tbest: 0.7610664 (30)\ttotal: 23.1s\tremaining: 5m 49s\n31:\ttest: 0.7608054\tbest: 0.7610664 (30)\ttotal: 23.8s\tremaining: 5m 47s\n32:\ttest: 0.7606344\tbest: 0.7610664 (30)\ttotal: 24.7s\tremaining: 5m 49s\n33:\ttest: 0.7609449\tbest: 0.7610664 (30)\ttotal: 25.5s\tremaining: 5m 49s\n34:\ttest: 0.7622992\tbest: 0.7622992 (34)\ttotal: 26.4s\tremaining: 5m 50s\n35:\ttest: 0.7632126\tbest: 0.7632126 (35)\ttotal: 27.2s\tremaining: 5m 50s\n36:\ttest: 0.7638695\tbest: 0.7638695 (36)\ttotal: 28.1s\tremaining: 5m 51s\n37:\ttest: 0.7644409\tbest: 0.7644409 (37)\ttotal: 28.8s\tremaining: 5m 49s\n38:\ttest: 0.7658583\tbest: 0.7658583 (38)\ttotal: 29.6s\tremaining: 5m 49s\n39:\ttest: 0.7650394\tbest: 0.7658583 (38)\ttotal: 30.5s\tremaining: 5m 50s\n40:\ttest: 0.7644274\tbest: 0.7658583 (38)\ttotal: 31.2s\tremaining: 5m 49s\n41:\ttest: 0.7646209\tbest: 0.7658583 (38)\ttotal: 32s\tremaining: 5m 48s\n42:\ttest: 0.7646884\tbest: 0.7658583 (38)\ttotal: 32.9s\tremaining: 5m 49s\n43:\ttest: 0.7639955\tbest: 0.7658583 (38)\ttotal: 33.7s\tremaining: 5m 49s\n44:\ttest: 0.7643375\tbest: 0.7658583 (38)\ttotal: 34.5s\tremaining: 5m 48s\n45:\ttest: 0.7639865\tbest: 0.7658583 (38)\ttotal: 35.3s\tremaining: 5m 48s\n46:\ttest: 0.7635816\tbest: 0.7658583 (38)\ttotal: 35.9s\tremaining: 5m 46s\n47:\ttest: 0.7645309\tbest: 0.7658583 (38)\ttotal: 36.7s\tremaining: 5m 45s\n48:\ttest: 0.7653003\tbest: 0.7658583 (38)\ttotal: 37.3s\tremaining: 5m 43s\n49:\ttest: 0.7660967\tbest: 0.7660967 (49)\ttotal: 38.2s\tremaining: 5m 43s\n50:\ttest: 0.7663712\tbest: 0.7663712 (50)\ttotal: 38.8s\tremaining: 5m 41s\n51:\ttest: 0.7661417\tbest: 0.7663712 (50)\ttotal: 39.5s\tremaining: 5m 40s\n52:\ttest: 0.7668481\tbest: 0.7668481 (52)\ttotal: 40.5s\tremaining: 5m 41s\n53:\ttest: 0.7679910\tbest: 0.7679910 (53)\ttotal: 41.3s\tremaining: 5m 41s\n54:\ttest: 0.7674286\tbest: 0.7679910 (53)\ttotal: 42.1s\tremaining: 5m 40s\n55:\ttest: 0.7672081\tbest: 0.7679910 (53)\ttotal: 42.9s\tremaining: 5m 40s\n56:\ttest: 0.7672891\tbest: 0.7679910 (53)\ttotal: 44s\tremaining: 5m 41s\n57:\ttest: 0.7681980\tbest: 0.7681980 (57)\ttotal: 45s\tremaining: 5m 42s\n58:\ttest: 0.7684229\tbest: 0.7684229 (58)\ttotal: 45.8s\tremaining: 5m 42s\n59:\ttest: 0.7680675\tbest: 0.7684229 (58)\ttotal: 46.6s\tremaining: 5m 41s\n60:\ttest: 0.7683780\tbest: 0.7684229 (58)\ttotal: 47.4s\tremaining: 5m 41s\n61:\ttest: 0.7687064\tbest: 0.7687064 (61)\ttotal: 48.5s\tremaining: 5m 42s\n62:\ttest: 0.7692643\tbest: 0.7692643 (62)\ttotal: 49.2s\tremaining: 5m 41s\n63:\ttest: 0.7695568\tbest: 0.7695568 (63)\ttotal: 49.9s\tremaining: 5m 39s\n64:\ttest: 0.7701147\tbest: 0.7701147 (64)\ttotal: 50.7s\tremaining: 5m 39s\n65:\ttest: 0.7701282\tbest: 0.7701282 (65)\ttotal: 51.7s\tremaining: 5m 39s\n66:\ttest: 0.7698178\tbest: 0.7701282 (65)\ttotal: 52.4s\tremaining: 5m 38s\n67:\ttest: 0.7698718\tbest: 0.7701282 (65)\ttotal: 53.1s\tremaining: 5m 37s\n68:\ttest: 0.7701957\tbest: 0.7701957 (68)\ttotal: 54.1s\tremaining: 5m 37s\n69:\ttest: 0.7701372\tbest: 0.7701957 (68)\ttotal: 55s\tremaining: 5m 37s\n70:\ttest: 0.7700832\tbest: 0.7701957 (68)\ttotal: 55.9s\tremaining: 5m 37s\n71:\ttest: 0.7701957\tbest: 0.7701957 (68)\ttotal: 56.7s\tremaining: 5m 37s\n72:\ttest: 0.7711406\tbest: 0.7711406 (72)\ttotal: 57.6s\tremaining: 5m 36s\n73:\ttest: 0.7710056\tbest: 0.7711406 (72)\ttotal: 58.6s\tremaining: 5m 37s\n74:\ttest: 0.7707402\tbest: 0.7711406 (72)\ttotal: 59.2s\tremaining: 5m 35s\n75:\ttest: 0.7703622\tbest: 0.7711406 (72)\ttotal: 1m\tremaining: 5m 35s\n76:\ttest: 0.7699393\tbest: 0.7711406 (72)\ttotal: 1m\tremaining: 5m 33s\n77:\ttest: 0.7703082\tbest: 0.7711406 (72)\ttotal: 1m 1s\tremaining: 5m 32s\n78:\ttest: 0.7699978\tbest: 0.7711406 (72)\ttotal: 1m 2s\tremaining: 5m 32s\n79:\ttest: 0.7695163\tbest: 0.7711406 (72)\ttotal: 1m 3s\tremaining: 5m 31s\n80:\ttest: 0.7693228\tbest: 0.7711406 (72)\ttotal: 1m 3s\tremaining: 5m 30s\n81:\ttest: 0.7693768\tbest: 0.7711406 (72)\ttotal: 1m 4s\tremaining: 5m 29s\n82:\ttest: 0.7694083\tbest: 0.7711406 (72)\ttotal: 1m 5s\tremaining: 5m 28s\n83:\ttest: 0.7692508\tbest: 0.7711406 (72)\ttotal: 1m 5s\tremaining: 5m 26s\n84:\ttest: 0.7695343\tbest: 0.7711406 (72)\ttotal: 1m 6s\tremaining: 5m 26s\n85:\ttest: 0.7689224\tbest: 0.7711406 (72)\ttotal: 1m 7s\tremaining: 5m 26s\n86:\ttest: 0.7687469\tbest: 0.7711406 (72)\ttotal: 1m 8s\tremaining: 5m 26s\n87:\ttest: 0.7687064\tbest: 0.7711406 (72)\ttotal: 1m 9s\tremaining: 5m 25s\n88:\ttest: 0.7687199\tbest: 0.7711406 (72)\ttotal: 1m 10s\tremaining: 5m 24s\n89:\ttest: 0.7689179\tbest: 0.7711406 (72)\ttotal: 1m 11s\tremaining: 5m 23s\n90:\ttest: 0.7688144\tbest: 0.7711406 (72)\ttotal: 1m 11s\tremaining: 5m 22s\n91:\ttest: 0.7687739\tbest: 0.7711406 (72)\ttotal: 1m 12s\tremaining: 5m 22s\n92:\ttest: 0.7685669\tbest: 0.7711406 (72)\ttotal: 1m 13s\tremaining: 5m 21s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7711406074\nbestIteration = 72\n\nShrink model to first 73 iterations.\n0:\ttest: 0.6830776\tbest: 0.6830776 (0)\ttotal: 990ms\tremaining: 8m 13s\n1:\ttest: 0.7376310\tbest: 0.7376310 (1)\ttotal: 1.6s\tremaining: 6m 37s\n2:\ttest: 0.8072868\tbest: 0.8072868 (2)\ttotal: 2.49s\tremaining: 6m 53s\n3:\ttest: 0.8054263\tbest: 0.8072868 (2)\ttotal: 3.4s\tremaining: 7m 1s\n4:\ttest: 0.8342857\tbest: 0.8342857 (4)\ttotal: 4.28s\tremaining: 7m 4s\n5:\ttest: 0.8444274\tbest: 0.8444274 (5)\ttotal: 5.09s\tremaining: 6m 59s\n6:\ttest: 0.8435771\tbest: 0.8444274 (5)\ttotal: 5.69s\tremaining: 6m 40s\n7:\ttest: 0.8434241\tbest: 0.8444274 (5)\ttotal: 6.49s\tremaining: 6m 39s\n8:\ttest: 0.8447334\tbest: 0.8447334 (8)\ttotal: 7.29s\tremaining: 6m 37s\n9:\ttest: 0.8447739\tbest: 0.8447739 (9)\ttotal: 8.09s\tremaining: 6m 36s\n10:\ttest: 0.8485422\tbest: 0.8485422 (10)\ttotal: 8.99s\tremaining: 6m 39s\n11:\ttest: 0.8467582\tbest: 0.8485422 (10)\ttotal: 9.78s\tremaining: 6m 37s\n12:\ttest: 0.8474668\tbest: 0.8485422 (10)\ttotal: 10.7s\tremaining: 6m 40s\n13:\ttest: 0.8490349\tbest: 0.8490349 (13)\ttotal: 11.6s\tremaining: 6m 42s\n14:\ttest: 0.8460967\tbest: 0.8490349 (13)\ttotal: 12.4s\tremaining: 6m 40s\n15:\ttest: 0.8497503\tbest: 0.8497503 (15)\ttotal: 13.3s\tremaining: 6m 41s\n16:\ttest: 0.8459123\tbest: 0.8497503 (15)\ttotal: 14.3s\tremaining: 6m 45s\n17:\ttest: 0.8495973\tbest: 0.8497503 (15)\ttotal: 14.8s\tremaining: 6m 35s\n18:\ttest: 0.8517120\tbest: 0.8517120 (18)\ttotal: 15.9s\tremaining: 6m 42s\n19:\ttest: 0.8534578\tbest: 0.8534578 (19)\ttotal: 16.8s\tremaining: 6m 42s\n20:\ttest: 0.8526569\tbest: 0.8534578 (19)\ttotal: 17.8s\tremaining: 6m 45s\n21:\ttest: 0.8469426\tbest: 0.8534578 (19)\ttotal: 18.5s\tremaining: 6m 41s\n22:\ttest: 0.8484859\tbest: 0.8534578 (19)\ttotal: 19.3s\tremaining: 6m 39s\n23:\ttest: 0.8476940\tbest: 0.8534578 (19)\ttotal: 20.1s\tremaining: 6m 38s\n24:\ttest: 0.8477705\tbest: 0.8534578 (19)\ttotal: 21s\tremaining: 6m 38s\n25:\ttest: 0.8478065\tbest: 0.8534578 (19)\ttotal: 21.9s\tremaining: 6m 39s\n26:\ttest: 0.8484319\tbest: 0.8534578 (19)\ttotal: 22.7s\tremaining: 6m 37s\n27:\ttest: 0.8473161\tbest: 0.8534578 (19)\ttotal: 23.4s\tremaining: 6m 34s\n28:\ttest: 0.8469606\tbest: 0.8534578 (19)\ttotal: 24.2s\tremaining: 6m 32s\n29:\ttest: 0.8456963\tbest: 0.8534578 (19)\ttotal: 25s\tremaining: 6m 31s\n30:\ttest: 0.8456783\tbest: 0.8534578 (19)\ttotal: 25.9s\tremaining: 6m 31s\n31:\ttest: 0.8448504\tbest: 0.8534578 (19)\ttotal: 26.8s\tremaining: 6m 31s\n32:\ttest: 0.8435411\tbest: 0.8534578 (19)\ttotal: 27.7s\tremaining: 6m 31s\n33:\ttest: 0.8441845\tbest: 0.8534578 (19)\ttotal: 28.4s\tremaining: 6m 29s\n34:\ttest: 0.8440810\tbest: 0.8534578 (19)\ttotal: 29.4s\tremaining: 6m 30s\n35:\ttest: 0.8437570\tbest: 0.8534578 (19)\ttotal: 30.3s\tremaining: 6m 30s\n36:\ttest: 0.8434196\tbest: 0.8534578 (19)\ttotal: 31.2s\tremaining: 6m 30s\n37:\ttest: 0.8435141\tbest: 0.8534578 (19)\ttotal: 32.1s\tremaining: 6m 29s\n38:\ttest: 0.8435276\tbest: 0.8534578 (19)\ttotal: 32.9s\tremaining: 6m 28s\n39:\ttest: 0.8436670\tbest: 0.8534578 (19)\ttotal: 33.8s\tremaining: 6m 28s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8534578178\nbestIteration = 19\n\nShrink model to first 20 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 685ms\tremaining: 5m 41s\n1:\ttest: 0.7489111\tbest: 0.7489111 (1)\ttotal: 1.39s\tremaining: 5m 46s\n2:\ttest: 0.7911789\tbest: 0.7911789 (2)\ttotal: 2s\tremaining: 5m 30s\n3:\ttest: 0.7907537\tbest: 0.7911789 (2)\ttotal: 2.59s\tremaining: 5m 21s\n4:\ttest: 0.8000967\tbest: 0.8000967 (4)\ttotal: 3.59s\tremaining: 5m 55s\n5:\ttest: 0.8103285\tbest: 0.8103285 (5)\ttotal: 4.49s\tremaining: 6m 9s\n6:\ttest: 0.8460427\tbest: 0.8460427 (6)\ttotal: 5.19s\tremaining: 6m 5s\n7:\ttest: 0.8445714\tbest: 0.8460427 (6)\ttotal: 5.99s\tremaining: 6m 8s\n8:\ttest: 0.8504342\tbest: 0.8504342 (8)\ttotal: 6.89s\tremaining: 6m 16s\n9:\ttest: 0.8471541\tbest: 0.8504342 (8)\ttotal: 7.99s\tremaining: 6m 31s\n10:\ttest: 0.8450439\tbest: 0.8504342 (8)\ttotal: 8.69s\tremaining: 6m 26s\n11:\ttest: 0.8471901\tbest: 0.8504342 (8)\ttotal: 9.29s\tremaining: 6m 17s\n12:\ttest: 0.8474376\tbest: 0.8504342 (8)\ttotal: 10s\tremaining: 6m 14s\n13:\ttest: 0.8484634\tbest: 0.8504342 (8)\ttotal: 11s\tremaining: 6m 21s\n14:\ttest: 0.8490304\tbest: 0.8504342 (8)\ttotal: 11.6s\tremaining: 6m 14s\n15:\ttest: 0.8500742\tbest: 0.8504342 (8)\ttotal: 12.5s\tremaining: 6m 17s\n16:\ttest: 0.8447784\tbest: 0.8504342 (8)\ttotal: 13.2s\tremaining: 6m 14s\n17:\ttest: 0.8452823\tbest: 0.8504342 (8)\ttotal: 13.9s\tremaining: 6m 12s\n18:\ttest: 0.8462047\tbest: 0.8504342 (8)\ttotal: 14.8s\tremaining: 6m 14s\n19:\ttest: 0.8455163\tbest: 0.8504342 (8)\ttotal: 15.7s\tremaining: 6m 16s\n20:\ttest: 0.8462272\tbest: 0.8504342 (8)\ttotal: 16.4s\tremaining: 6m 13s\n21:\ttest: 0.8434961\tbest: 0.8504342 (8)\ttotal: 17.2s\tremaining: 6m 13s\n22:\ttest: 0.8443420\tbest: 0.8504342 (8)\ttotal: 17.8s\tremaining: 6m 9s\n23:\ttest: 0.8440945\tbest: 0.8504342 (8)\ttotal: 18.6s\tremaining: 6m 9s\n24:\ttest: 0.8410034\tbest: 0.8504342 (8)\ttotal: 19.2s\tremaining: 6m 4s\n25:\ttest: 0.8391721\tbest: 0.8504342 (8)\ttotal: 20.2s\tremaining: 6m 8s\n26:\ttest: 0.8386457\tbest: 0.8504342 (8)\ttotal: 21s\tremaining: 6m 8s\n27:\ttest: 0.8386322\tbest: 0.8504342 (8)\ttotal: 21.7s\tremaining: 6m 5s\n28:\ttest: 0.8362610\tbest: 0.8504342 (8)\ttotal: 22.4s\tremaining: 6m 3s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8504341957\nbestIteration = 8\n\nShrink model to first 9 iterations.\n0:\ttest: 0.6545534\tbest: 0.6545534 (0)\ttotal: 894ms\tremaining: 7m 26s\n1:\ttest: 0.6774286\tbest: 0.6774286 (1)\ttotal: 1.6s\tremaining: 6m 39s\n2:\ttest: 0.6692126\tbest: 0.6774286 (1)\ttotal: 2.3s\tremaining: 6m 21s\n3:\ttest: 0.6710236\tbest: 0.6774286 (1)\ttotal: 3s\tremaining: 6m 12s\n4:\ttest: 0.7297818\tbest: 0.7297818 (4)\ttotal: 3.69s\tremaining: 6m 5s\n5:\ttest: 0.7305152\tbest: 0.7305152 (5)\ttotal: 4.3s\tremaining: 5m 53s\n6:\ttest: 0.7488616\tbest: 0.7488616 (6)\ttotal: 5.19s\tremaining: 6m 5s\n7:\ttest: 0.7511564\tbest: 0.7511564 (7)\ttotal: 5.9s\tremaining: 6m 3s\n8:\ttest: 0.7501822\tbest: 0.7511564 (7)\ttotal: 6.79s\tremaining: 6m 10s\n9:\ttest: 0.7531721\tbest: 0.7531721 (9)\ttotal: 7.69s\tremaining: 6m 17s\n10:\ttest: 0.7542835\tbest: 0.7542835 (10)\ttotal: 8.5s\tremaining: 6m 17s\n11:\ttest: 0.7520517\tbest: 0.7542835 (10)\ttotal: 9.1s\tremaining: 6m 10s\n12:\ttest: 0.7518943\tbest: 0.7542835 (10)\ttotal: 9.8s\tremaining: 6m 7s\n13:\ttest: 0.7539055\tbest: 0.7542835 (10)\ttotal: 10.6s\tremaining: 6m 7s\n14:\ttest: 0.7550124\tbest: 0.7550124 (14)\ttotal: 11.3s\tremaining: 6m 5s\n15:\ttest: 0.7530101\tbest: 0.7550124 (14)\ttotal: 12.1s\tremaining: 6m 5s\n16:\ttest: 0.7523127\tbest: 0.7550124 (14)\ttotal: 12.7s\tremaining: 6m\n17:\ttest: 0.7522452\tbest: 0.7550124 (14)\ttotal: 13.2s\tremaining: 5m 53s\n18:\ttest: 0.7518965\tbest: 0.7550124 (14)\ttotal: 13.9s\tremaining: 5m 51s\n19:\ttest: 0.7542812\tbest: 0.7550124 (14)\ttotal: 14.6s\tremaining: 5m 50s\n20:\ttest: 0.7534893\tbest: 0.7550124 (14)\ttotal: 15.4s\tremaining: 5m 51s\n21:\ttest: 0.7538538\tbest: 0.7550124 (14)\ttotal: 16.3s\tremaining: 5m 53s\n22:\ttest: 0.7548256\tbest: 0.7550124 (14)\ttotal: 17.1s\tremaining: 5m 54s\n23:\ttest: 0.7563330\tbest: 0.7563330 (23)\ttotal: 17.7s\tremaining: 5m 50s\n24:\ttest: 0.7575073\tbest: 0.7575073 (24)\ttotal: 18.4s\tremaining: 5m 49s\n25:\ttest: 0.7592126\tbest: 0.7592126 (25)\ttotal: 19.1s\tremaining: 5m 48s\n26:\ttest: 0.7594466\tbest: 0.7594466 (26)\ttotal: 19.8s\tremaining: 5m 46s\n27:\ttest: 0.7600180\tbest: 0.7600180 (27)\ttotal: 20.4s\tremaining: 5m 43s\n28:\ttest: 0.7601980\tbest: 0.7601980 (28)\ttotal: 21.2s\tremaining: 5m 44s\n29:\ttest: 0.7603330\tbest: 0.7603330 (29)\ttotal: 21.9s\tremaining: 5m 42s\n30:\ttest: 0.7623712\tbest: 0.7623712 (30)\ttotal: 22.6s\tremaining: 5m 41s\n31:\ttest: 0.7621552\tbest: 0.7623712 (30)\ttotal: 23.2s\tremaining: 5m 39s\n32:\ttest: 0.7607514\tbest: 0.7623712 (30)\ttotal: 23.8s\tremaining: 5m 36s\n33:\ttest: 0.7604094\tbest: 0.7623712 (30)\ttotal: 24.4s\tremaining: 5m 34s\n34:\ttest: 0.7593611\tbest: 0.7623712 (30)\ttotal: 25.1s\tremaining: 5m 33s\n35:\ttest: 0.7591811\tbest: 0.7623712 (30)\ttotal: 25.9s\tremaining: 5m 33s\n36:\ttest: 0.7585827\tbest: 0.7623712 (30)\ttotal: 26.7s\tremaining: 5m 34s\n37:\ttest: 0.7588481\tbest: 0.7623712 (30)\ttotal: 27.3s\tremaining: 5m 31s\n38:\ttest: 0.7591001\tbest: 0.7623712 (30)\ttotal: 28s\tremaining: 5m 31s\n39:\ttest: 0.7592126\tbest: 0.7623712 (30)\ttotal: 28.9s\tremaining: 5m 32s\n40:\ttest: 0.7584657\tbest: 0.7623712 (30)\ttotal: 29.6s\tremaining: 5m 31s\n41:\ttest: 0.7585602\tbest: 0.7623712 (30)\ttotal: 30.3s\tremaining: 5m 30s\n42:\ttest: 0.7584972\tbest: 0.7623712 (30)\ttotal: 30.9s\tremaining: 5m 28s\n43:\ttest: 0.7583757\tbest: 0.7623712 (30)\ttotal: 31.7s\tremaining: 5m 28s\n44:\ttest: 0.7583037\tbest: 0.7623712 (30)\ttotal: 32.5s\tremaining: 5m 28s\n45:\ttest: 0.7584747\tbest: 0.7623712 (30)\ttotal: 33.4s\tremaining: 5m 29s\n46:\ttest: 0.7582812\tbest: 0.7623712 (30)\ttotal: 34.1s\tremaining: 5m 28s\n47:\ttest: 0.7579978\tbest: 0.7623712 (30)\ttotal: 34.9s\tremaining: 5m 28s\n48:\ttest: 0.7586457\tbest: 0.7623712 (30)\ttotal: 35.8s\tremaining: 5m 29s\n49:\ttest: 0.7587672\tbest: 0.7623712 (30)\ttotal: 36.7s\tremaining: 5m 30s\n50:\ttest: 0.7587402\tbest: 0.7623712 (30)\ttotal: 37.6s\tremaining: 5m 31s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7623712036\nbestIteration = 30\n\nShrink model to first 31 iterations.\n0:\ttest: 0.6830776\tbest: 0.6830776 (0)\ttotal: 690ms\tremaining: 5m 44s\n1:\ttest: 0.7373251\tbest: 0.7373251 (1)\ttotal: 1.39s\tremaining: 5m 46s\n2:\ttest: 0.8076468\tbest: 0.8076468 (2)\ttotal: 2.19s\tremaining: 6m 2s\n3:\ttest: 0.8056378\tbest: 0.8076468 (2)\ttotal: 2.9s\tremaining: 5m 59s\n4:\ttest: 0.8474758\tbest: 0.8474758 (4)\ttotal: 3.69s\tremaining: 6m 5s\n5:\ttest: 0.8562655\tbest: 0.8562655 (5)\ttotal: 4.39s\tremaining: 6m 1s\n6:\ttest: 0.8572463\tbest: 0.8572463 (6)\ttotal: 4.99s\tremaining: 5m 51s\n7:\ttest: 0.8519798\tbest: 0.8572463 (6)\ttotal: 5.49s\tremaining: 5m 37s\n8:\ttest: 0.8520967\tbest: 0.8572463 (6)\ttotal: 6.29s\tremaining: 5m 43s\n9:\ttest: 0.8500765\tbest: 0.8572463 (6)\ttotal: 7.09s\tremaining: 5m 47s\n10:\ttest: 0.8522362\tbest: 0.8572463 (6)\ttotal: 7.78s\tremaining: 5m 45s\n11:\ttest: 0.8532171\tbest: 0.8572463 (6)\ttotal: 8.29s\tremaining: 5m 37s\n12:\ttest: 0.8517773\tbest: 0.8572463 (6)\ttotal: 9.09s\tremaining: 5m 40s\n13:\ttest: 0.8530529\tbest: 0.8572463 (6)\ttotal: 9.99s\tremaining: 5m 46s\n14:\ttest: 0.8522250\tbest: 0.8572463 (6)\ttotal: 10.7s\tremaining: 5m 45s\n15:\ttest: 0.8485804\tbest: 0.8572463 (6)\ttotal: 11.4s\tremaining: 5m 44s\n16:\ttest: 0.8481395\tbest: 0.8572463 (6)\ttotal: 12s\tremaining: 5m 40s\n17:\ttest: 0.8431631\tbest: 0.8572463 (6)\ttotal: 12.7s\tremaining: 5m 39s\n18:\ttest: 0.8419213\tbest: 0.8572463 (6)\ttotal: 14s\tremaining: 5m 53s\n19:\ttest: 0.8422227\tbest: 0.8572463 (6)\ttotal: 14.7s\tremaining: 5m 52s\n20:\ttest: 0.8388706\tbest: 0.8572463 (6)\ttotal: 15.5s\tremaining: 5m 53s\n21:\ttest: 0.8391226\tbest: 0.8572463 (6)\ttotal: 16.3s\tremaining: 5m 53s\n22:\ttest: 0.8393566\tbest: 0.8572463 (6)\ttotal: 17s\tremaining: 5m 52s\n23:\ttest: 0.8402790\tbest: 0.8572463 (6)\ttotal: 17.6s\tremaining: 5m 48s\n24:\ttest: 0.8386142\tbest: 0.8572463 (6)\ttotal: 18.3s\tremaining: 5m 47s\n25:\ttest: 0.8392936\tbest: 0.8572463 (6)\ttotal: 19.2s\tremaining: 5m 49s\n26:\ttest: 0.8400720\tbest: 0.8572463 (6)\ttotal: 20s\tremaining: 5m 49s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8572463442\nbestIteration = 6\n\nShrink model to first 7 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 911ms\tremaining: 7m 34s\n1:\ttest: 0.8075681\tbest: 0.8075681 (1)\ttotal: 1.61s\tremaining: 6m 40s\n2:\ttest: 0.8222070\tbest: 0.8222070 (2)\ttotal: 2.4s\tremaining: 6m 37s\n3:\ttest: 0.8222677\tbest: 0.8222677 (3)\ttotal: 3.2s\tremaining: 6m 37s\n4:\ttest: 0.8280202\tbest: 0.8280202 (4)\ttotal: 3.71s\tremaining: 6m 7s\n5:\ttest: 0.8287064\tbest: 0.8287064 (5)\ttotal: 4.6s\tremaining: 6m 18s\n6:\ttest: 0.8287469\tbest: 0.8287469 (6)\ttotal: 5.3s\tremaining: 6m 13s\n7:\ttest: 0.8171204\tbest: 0.8287469 (6)\ttotal: 6.01s\tremaining: 6m 9s\n8:\ttest: 0.8234848\tbest: 0.8287469 (6)\ttotal: 6.7s\tremaining: 6m 5s\n9:\ttest: 0.8228144\tbest: 0.8287469 (6)\ttotal: 7.5s\tremaining: 6m 7s\n10:\ttest: 0.8263802\tbest: 0.8287469 (6)\ttotal: 8.21s\tremaining: 6m 4s\n11:\ttest: 0.8312441\tbest: 0.8312441 (11)\ttotal: 8.9s\tremaining: 6m 2s\n12:\ttest: 0.8293498\tbest: 0.8312441 (11)\ttotal: 9.59s\tremaining: 5m 59s\n13:\ttest: 0.8323420\tbest: 0.8323420 (13)\ttotal: 10.4s\tremaining: 6m 1s\n14:\ttest: 0.8346682\tbest: 0.8346682 (14)\ttotal: 11.2s\tremaining: 6m 2s\n15:\ttest: 0.8341597\tbest: 0.8346682 (14)\ttotal: 11.8s\tremaining: 5m 57s\n16:\ttest: 0.8347357\tbest: 0.8347357 (16)\ttotal: 12.5s\tremaining: 5m 55s\n17:\ttest: 0.8351541\tbest: 0.8351541 (17)\ttotal: 13.2s\tremaining: 5m 53s\n18:\ttest: 0.8209359\tbest: 0.8351541 (17)\ttotal: 13.8s\tremaining: 5m 49s\n19:\ttest: 0.8208549\tbest: 0.8351541 (17)\ttotal: 14.7s\tremaining: 5m 52s\n20:\ttest: 0.8217548\tbest: 0.8351541 (17)\ttotal: 15.5s\tremaining: 5m 53s\n21:\ttest: 0.8119730\tbest: 0.8351541 (17)\ttotal: 16.5s\tremaining: 5m 58s\n22:\ttest: 0.8130034\tbest: 0.8351541 (17)\ttotal: 17.3s\tremaining: 5m 58s\n23:\ttest: 0.8139753\tbest: 0.8351541 (17)\ttotal: 18s\tremaining: 5m 56s\n24:\ttest: 0.8138853\tbest: 0.8351541 (17)\ttotal: 18.7s\tremaining: 5m 55s\n25:\ttest: 0.8166164\tbest: 0.8351541 (17)\ttotal: 19.2s\tremaining: 5m 50s\n26:\ttest: 0.8166074\tbest: 0.8351541 (17)\ttotal: 20.2s\tremaining: 5m 53s\n27:\ttest: 0.8176558\tbest: 0.8351541 (17)\ttotal: 21.2s\tremaining: 5m 57s\n28:\ttest: 0.8187582\tbest: 0.8351541 (17)\ttotal: 22s\tremaining: 5m 57s\n29:\ttest: 0.8193971\tbest: 0.8351541 (17)\ttotal: 22.7s\tremaining: 5m 55s\n30:\ttest: 0.8202835\tbest: 0.8351541 (17)\ttotal: 23.5s\tremaining: 5m 55s\n31:\ttest: 0.8211024\tbest: 0.8351541 (17)\ttotal: 24.3s\tremaining: 5m 55s\n32:\ttest: 0.8223307\tbest: 0.8351541 (17)\ttotal: 25.1s\tremaining: 5m 55s\n33:\ttest: 0.8221417\tbest: 0.8351541 (17)\ttotal: 25.9s\tremaining: 5m 54s\n34:\ttest: 0.8219843\tbest: 0.8351541 (17)\ttotal: 26.8s\tremaining: 5m 55s\n35:\ttest: 0.8229336\tbest: 0.8351541 (17)\ttotal: 27.6s\tremaining: 5m 55s\n36:\ttest: 0.8243645\tbest: 0.8351541 (17)\ttotal: 28.4s\tremaining: 5m 55s\n37:\ttest: 0.8241620\tbest: 0.8351541 (17)\ttotal: 29.3s\tremaining: 5m 56s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8351541057\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.6539235\tbest: 0.6539235 (0)\ttotal: 700ms\tremaining: 5m 49s\n1:\ttest: 0.6774196\tbest: 0.6774196 (1)\ttotal: 1.6s\tremaining: 6m 39s\n2:\ttest: 0.6934308\tbest: 0.6934308 (2)\ttotal: 2.4s\tremaining: 6m 37s\n3:\ttest: 0.6988031\tbest: 0.6988031 (3)\ttotal: 3.4s\tremaining: 7m 1s\n4:\ttest: 0.7580922\tbest: 0.7580922 (4)\ttotal: 4.2s\tremaining: 6m 55s\n5:\ttest: 0.7722565\tbest: 0.7722565 (5)\ttotal: 5s\tremaining: 6m 51s\n6:\ttest: 0.7720292\tbest: 0.7722565 (5)\ttotal: 5.89s\tremaining: 6m 54s\n7:\ttest: 0.7728391\tbest: 0.7728391 (7)\ttotal: 6.7s\tremaining: 6m 52s\n8:\ttest: 0.7669111\tbest: 0.7728391 (7)\ttotal: 7.41s\tremaining: 6m 44s\n9:\ttest: 0.7637548\tbest: 0.7728391 (7)\ttotal: 8.19s\tremaining: 6m 41s\n10:\ttest: 0.7621192\tbest: 0.7728391 (7)\ttotal: 8.89s\tremaining: 6m 35s\n11:\ttest: 0.7650101\tbest: 0.7728391 (7)\ttotal: 9.79s\tremaining: 6m 38s\n12:\ttest: 0.7653566\tbest: 0.7728391 (7)\ttotal: 10.6s\tremaining: 6m 37s\n13:\ttest: 0.7652306\tbest: 0.7728391 (7)\ttotal: 11.3s\tremaining: 6m 31s\n14:\ttest: 0.7667717\tbest: 0.7728391 (7)\ttotal: 12.1s\tremaining: 6m 31s\n15:\ttest: 0.7658988\tbest: 0.7728391 (7)\ttotal: 12.8s\tremaining: 6m 27s\n16:\ttest: 0.7634781\tbest: 0.7728391 (7)\ttotal: 13.6s\tremaining: 6m 26s\n17:\ttest: 0.7634961\tbest: 0.7728391 (7)\ttotal: 14.3s\tremaining: 6m 23s\n18:\ttest: 0.7635096\tbest: 0.7728391 (7)\ttotal: 15s\tremaining: 6m 19s\n19:\ttest: 0.7610214\tbest: 0.7728391 (7)\ttotal: 15.8s\tremaining: 6m 18s\n20:\ttest: 0.7591946\tbest: 0.7728391 (7)\ttotal: 16.5s\tremaining: 6m 15s\n21:\ttest: 0.7585197\tbest: 0.7728391 (7)\ttotal: 17s\tremaining: 6m 9s\n22:\ttest: 0.7613273\tbest: 0.7728391 (7)\ttotal: 17.8s\tremaining: 6m 8s\n23:\ttest: 0.7615748\tbest: 0.7728391 (7)\ttotal: 18.6s\tremaining: 6m 8s\n24:\ttest: 0.7621372\tbest: 0.7728391 (7)\ttotal: 19.4s\tremaining: 6m 8s\n25:\ttest: 0.7624387\tbest: 0.7728391 (7)\ttotal: 20.2s\tremaining: 6m 8s\n26:\ttest: 0.7627717\tbest: 0.7728391 (7)\ttotal: 20.9s\tremaining: 6m 6s\n27:\ttest: 0.7628931\tbest: 0.7728391 (7)\ttotal: 21.5s\tremaining: 6m 2s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7728391451\nbestIteration = 7\n\nShrink model to first 8 iterations.\n0:\ttest: 0.6782272\tbest: 0.6782272 (0)\ttotal: 595ms\tremaining: 4m 57s\n1:\ttest: 0.7005759\tbest: 0.7005759 (1)\ttotal: 1.2s\tremaining: 4m 59s\n2:\ttest: 0.7714398\tbest: 0.7714398 (2)\ttotal: 1.8s\tremaining: 4m 58s\n3:\ttest: 0.7697998\tbest: 0.7714398 (2)\ttotal: 2.31s\tremaining: 4m 46s\n4:\ttest: 0.8180517\tbest: 0.8180517 (4)\ttotal: 3.1s\tremaining: 5m 7s\n5:\ttest: 0.8333453\tbest: 0.8333453 (5)\ttotal: 3.8s\tremaining: 5m 12s\n6:\ttest: 0.8299280\tbest: 0.8333453 (5)\ttotal: 4.39s\tremaining: 5m 9s\n7:\ttest: 0.8342317\tbest: 0.8342317 (7)\ttotal: 4.79s\tremaining: 4m 54s\n8:\ttest: 0.8372981\tbest: 0.8372981 (8)\ttotal: 5.29s\tremaining: 4m 48s\n9:\ttest: 0.8373746\tbest: 0.8373746 (9)\ttotal: 5.89s\tremaining: 4m 48s\n10:\ttest: 0.8455231\tbest: 0.8455231 (10)\ttotal: 6.5s\tremaining: 4m 48s\n11:\ttest: 0.8438425\tbest: 0.8455231 (10)\ttotal: 7.2s\tremaining: 4m 52s\n12:\ttest: 0.8438200\tbest: 0.8455231 (10)\ttotal: 7.99s\tremaining: 4m 59s\n13:\ttest: 0.8427897\tbest: 0.8455231 (10)\ttotal: 8.61s\tremaining: 4m 58s\n14:\ttest: 0.8421642\tbest: 0.8455231 (10)\ttotal: 9.3s\tremaining: 5m\n15:\ttest: 0.8406434\tbest: 0.8455231 (10)\ttotal: 9.9s\tremaining: 4m 59s\n16:\ttest: 0.8425647\tbest: 0.8455231 (10)\ttotal: 10.7s\tremaining: 5m 3s\n17:\ttest: 0.8493633\tbest: 0.8493633 (17)\ttotal: 11.3s\tremaining: 5m 2s\n18:\ttest: 0.8472891\tbest: 0.8493633 (17)\ttotal: 11.9s\tremaining: 5m 1s\n19:\ttest: 0.8423307\tbest: 0.8493633 (17)\ttotal: 12.7s\tremaining: 5m 4s\n20:\ttest: 0.8429156\tbest: 0.8493633 (17)\ttotal: 13.3s\tremaining: 5m 3s\n21:\ttest: 0.8412058\tbest: 0.8493633 (17)\ttotal: 14s\tremaining: 5m 3s\n22:\ttest: 0.8444814\tbest: 0.8493633 (17)\ttotal: 14.6s\tremaining: 5m 2s\n23:\ttest: 0.8445759\tbest: 0.8493633 (17)\ttotal: 15.2s\tremaining: 5m 1s\n24:\ttest: 0.8443150\tbest: 0.8493633 (17)\ttotal: 15.6s\tremaining: 4m 56s\n25:\ttest: 0.8475006\tbest: 0.8493633 (17)\ttotal: 16.2s\tremaining: 4m 55s\n26:\ttest: 0.8487379\tbest: 0.8493633 (17)\ttotal: 16.8s\tremaining: 4m 54s\n27:\ttest: 0.8481620\tbest: 0.8493633 (17)\ttotal: 17.6s\tremaining: 4m 56s\n28:\ttest: 0.8467537\tbest: 0.8493633 (17)\ttotal: 18.1s\tremaining: 4m 53s\n29:\ttest: 0.8461327\tbest: 0.8493633 (17)\ttotal: 18.7s\tremaining: 4m 52s\n30:\ttest: 0.8455118\tbest: 0.8493633 (17)\ttotal: 19.4s\tremaining: 4m 53s\n31:\ttest: 0.8452778\tbest: 0.8493633 (17)\ttotal: 20s\tremaining: 4m 52s\n32:\ttest: 0.8468931\tbest: 0.8493633 (17)\ttotal: 20.6s\tremaining: 4m 51s\n33:\ttest: 0.8469336\tbest: 0.8493633 (17)\ttotal: 21.3s\tremaining: 4m 51s\n34:\ttest: 0.8471856\tbest: 0.8493633 (17)\ttotal: 21.9s\tremaining: 4m 50s\n35:\ttest: 0.8468616\tbest: 0.8493633 (17)\ttotal: 22.5s\tremaining: 4m 49s\n36:\ttest: 0.8449269\tbest: 0.8493633 (17)\ttotal: 23.2s\tremaining: 4m 50s\n37:\ttest: 0.8448684\tbest: 0.8493633 (17)\ttotal: 23.6s\tremaining: 4m 46s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8493633296\nbestIteration = 17\n\nShrink model to first 18 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 687ms\tremaining: 5m 43s\n1:\ttest: 0.7137255\tbest: 0.7137255 (1)\ttotal: 1.39s\tremaining: 5m 46s\n2:\ttest: 0.7687469\tbest: 0.7687469 (2)\ttotal: 2.19s\tremaining: 6m 3s\n3:\ttest: 0.7694466\tbest: 0.7694466 (3)\ttotal: 2.8s\tremaining: 5m 46s\n4:\ttest: 0.8148819\tbest: 0.8148819 (4)\ttotal: 3.6s\tremaining: 5m 55s\n5:\ttest: 0.8280360\tbest: 0.8280360 (5)\ttotal: 4.3s\tremaining: 5m 54s\n6:\ttest: 0.8379280\tbest: 0.8379280 (6)\ttotal: 4.99s\tremaining: 5m 51s\n7:\ttest: 0.8404859\tbest: 0.8404859 (7)\ttotal: 5.69s\tremaining: 5m 50s\n8:\ttest: 0.8416378\tbest: 0.8416378 (8)\ttotal: 6.39s\tremaining: 5m 48s\n9:\ttest: 0.8416468\tbest: 0.8416468 (9)\ttotal: 7.38s\tremaining: 6m 1s\n10:\ttest: 0.8487852\tbest: 0.8487852 (10)\ttotal: 7.9s\tremaining: 5m 51s\n11:\ttest: 0.8471564\tbest: 0.8487852 (10)\ttotal: 8.99s\tremaining: 6m 5s\n12:\ttest: 0.8453926\tbest: 0.8487852 (10)\ttotal: 9.68s\tremaining: 6m 2s\n13:\ttest: 0.8447559\tbest: 0.8487852 (10)\ttotal: 10.4s\tremaining: 6m\n14:\ttest: 0.8479190\tbest: 0.8487852 (10)\ttotal: 11.2s\tremaining: 6m 1s\n15:\ttest: 0.8476310\tbest: 0.8487852 (10)\ttotal: 12s\tremaining: 6m 2s\n16:\ttest: 0.8462137\tbest: 0.8487852 (10)\ttotal: 12.8s\tremaining: 6m 3s\n17:\ttest: 0.8459168\tbest: 0.8487852 (10)\ttotal: 13.5s\tremaining: 6m 1s\n18:\ttest: 0.8456198\tbest: 0.8487852 (10)\ttotal: 14.2s\tremaining: 5m 58s\n19:\ttest: 0.8471811\tbest: 0.8487852 (10)\ttotal: 14.9s\tremaining: 5m 57s\n20:\ttest: 0.8441350\tbest: 0.8487852 (10)\ttotal: 15.7s\tremaining: 5m 57s\n21:\ttest: 0.8407289\tbest: 0.8487852 (10)\ttotal: 16.5s\tremaining: 5m 57s\n22:\ttest: 0.8402205\tbest: 0.8487852 (10)\ttotal: 17.2s\tremaining: 5m 56s\n23:\ttest: 0.8405174\tbest: 0.8487852 (10)\ttotal: 17.9s\tremaining: 5m 54s\n24:\ttest: 0.8402295\tbest: 0.8487852 (10)\ttotal: 18.6s\tremaining: 5m 52s\n25:\ttest: 0.8378268\tbest: 0.8487852 (10)\ttotal: 19.3s\tremaining: 5m 51s\n26:\ttest: 0.8349471\tbest: 0.8487852 (10)\ttotal: 20s\tremaining: 5m 50s\n27:\ttest: 0.8349426\tbest: 0.8487852 (10)\ttotal: 20.8s\tremaining: 5m 50s\n28:\ttest: 0.8363825\tbest: 0.8487852 (10)\ttotal: 21.5s\tremaining: 5m 48s\n29:\ttest: 0.8362610\tbest: 0.8487852 (10)\ttotal: 22.6s\tremaining: 5m 53s\n30:\ttest: 0.8329449\tbest: 0.8487852 (10)\ttotal: 23.4s\tremaining: 5m 53s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8487851519\nbestIteration = 10\n\nShrink model to first 11 iterations.\n0:\ttest: 0.6539235\tbest: 0.6539235 (0)\ttotal: 605ms\tremaining: 5m 1s\n1:\ttest: 0.6773206\tbest: 0.6773206 (1)\ttotal: 1.3s\tremaining: 5m 24s\n2:\ttest: 0.6921935\tbest: 0.6921935 (2)\ttotal: 1.99s\tremaining: 5m 30s\n3:\ttest: 0.6884049\tbest: 0.6921935 (2)\ttotal: 2.5s\tremaining: 5m 9s\n4:\ttest: 0.7251001\tbest: 0.7251001 (4)\ttotal: 3.1s\tremaining: 5m 6s\n5:\ttest: 0.7591676\tbest: 0.7591676 (5)\ttotal: 3.8s\tremaining: 5m 12s\n6:\ttest: 0.7585624\tbest: 0.7591676 (5)\ttotal: 4.39s\tremaining: 5m 9s\n7:\ttest: 0.7529021\tbest: 0.7591676 (5)\ttotal: 5.09s\tremaining: 5m 13s\n8:\ttest: 0.7534758\tbest: 0.7591676 (5)\ttotal: 5.7s\tremaining: 5m 10s\n9:\ttest: 0.7510349\tbest: 0.7591676 (5)\ttotal: 6.39s\tremaining: 5m 12s\n10:\ttest: 0.7522655\tbest: 0.7591676 (5)\ttotal: 6.8s\tremaining: 5m 2s\n11:\ttest: 0.7571879\tbest: 0.7591676 (5)\ttotal: 7.4s\tremaining: 5m\n12:\ttest: 0.7560945\tbest: 0.7591676 (5)\ttotal: 7.99s\tremaining: 4m 59s\n13:\ttest: 0.7560900\tbest: 0.7591676 (5)\ttotal: 8.69s\tremaining: 5m 1s\n14:\ttest: 0.7588549\tbest: 0.7591676 (5)\ttotal: 9.39s\tremaining: 5m 3s\n15:\ttest: 0.7593588\tbest: 0.7593588 (15)\ttotal: 10.1s\tremaining: 5m 5s\n16:\ttest: 0.7576625\tbest: 0.7593588 (15)\ttotal: 10.9s\tremaining: 5m 9s\n17:\ttest: 0.7575006\tbest: 0.7593588 (15)\ttotal: 11.6s\tremaining: 5m 10s\n18:\ttest: 0.7579640\tbest: 0.7593588 (15)\ttotal: 12.2s\tremaining: 5m 8s\n19:\ttest: 0.7586839\tbest: 0.7593588 (15)\ttotal: 12.7s\tremaining: 5m 4s\n20:\ttest: 0.7573566\tbest: 0.7593588 (15)\ttotal: 13.3s\tremaining: 5m 3s\n21:\ttest: 0.7625152\tbest: 0.7625152 (21)\ttotal: 14s\tremaining: 5m 3s\n22:\ttest: 0.7632801\tbest: 0.7632801 (22)\ttotal: 14.5s\tremaining: 5m\n23:\ttest: 0.7628211\tbest: 0.7632801 (22)\ttotal: 15.3s\tremaining: 5m 3s\n24:\ttest: 0.7628751\tbest: 0.7632801 (22)\ttotal: 16s\tremaining: 5m 3s\n25:\ttest: 0.7634421\tbest: 0.7634421 (25)\ttotal: 16.7s\tremaining: 5m 4s\n26:\ttest: 0.7651924\tbest: 0.7651924 (26)\ttotal: 17.4s\tremaining: 5m 4s\n27:\ttest: 0.7654038\tbest: 0.7654038 (27)\ttotal: 18.1s\tremaining: 5m 4s\n28:\ttest: 0.7659753\tbest: 0.7659753 (28)\ttotal: 18.7s\tremaining: 5m 3s\n29:\ttest: 0.7660157\tbest: 0.7660157 (29)\ttotal: 19.4s\tremaining: 5m 3s\n30:\ttest: 0.7659033\tbest: 0.7660157 (29)\ttotal: 20.1s\tremaining: 5m 3s\n31:\ttest: 0.7656153\tbest: 0.7660157 (29)\ttotal: 20.8s\tremaining: 5m 3s\n32:\ttest: 0.7664072\tbest: 0.7664072 (32)\ttotal: 21.3s\tremaining: 5m 1s\n33:\ttest: 0.7670551\tbest: 0.7670551 (33)\ttotal: 21.9s\tremaining: 4m 59s\n34:\ttest: 0.7670596\tbest: 0.7670596 (34)\ttotal: 22.5s\tremaining: 4m 58s\n35:\ttest: 0.7671631\tbest: 0.7671631 (35)\ttotal: 23s\tremaining: 4m 56s\n36:\ttest: 0.7676265\tbest: 0.7676265 (36)\ttotal: 23.7s\tremaining: 4m 56s\n37:\ttest: 0.7689989\tbest: 0.7689989 (37)\ttotal: 24.3s\tremaining: 4m 55s\n38:\ttest: 0.7699168\tbest: 0.7699168 (38)\ttotal: 25s\tremaining: 4m 55s\n39:\ttest: 0.7699123\tbest: 0.7699168 (38)\ttotal: 25.6s\tremaining: 4m 54s\n40:\ttest: 0.7695478\tbest: 0.7699168 (38)\ttotal: 26.4s\tremaining: 4m 55s\n41:\ttest: 0.7698943\tbest: 0.7699168 (38)\ttotal: 27.2s\tremaining: 4m 56s\n42:\ttest: 0.7697143\tbest: 0.7699168 (38)\ttotal: 28s\tremaining: 4m 57s\n43:\ttest: 0.7693993\tbest: 0.7699168 (38)\ttotal: 28.8s\tremaining: 4m 58s\n44:\ttest: 0.7701597\tbest: 0.7701597 (44)\ttotal: 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38.4s\tremaining: 4m 52s\n58:\ttest: 0.7743397\tbest: 0.7752171 (54)\ttotal: 38.9s\tremaining: 4m 50s\n59:\ttest: 0.7744297\tbest: 0.7752171 (54)\ttotal: 39.4s\tremaining: 4m 48s\n60:\ttest: 0.7749966\tbest: 0.7752171 (54)\ttotal: 40.1s\tremaining: 4m 48s\n61:\ttest: 0.7753386\tbest: 0.7753386 (61)\ttotal: 40.8s\tremaining: 4m 48s\n62:\ttest: 0.7754241\tbest: 0.7754241 (62)\ttotal: 41.5s\tremaining: 4m 47s\n63:\ttest: 0.7754421\tbest: 0.7754421 (63)\ttotal: 42.1s\tremaining: 4m 46s\n64:\ttest: 0.7755951\tbest: 0.7755951 (64)\ttotal: 42.7s\tremaining: 4m 45s\n65:\ttest: 0.7751541\tbest: 0.7755951 (64)\ttotal: 43.3s\tremaining: 4m 44s\n66:\ttest: 0.7751406\tbest: 0.7755951 (64)\ttotal: 43.9s\tremaining: 4m 43s\n67:\ttest: 0.7751181\tbest: 0.7755951 (64)\ttotal: 44.4s\tremaining: 4m 41s\n68:\ttest: 0.7748526\tbest: 0.7755951 (64)\ttotal: 45.1s\tremaining: 4m 41s\n69:\ttest: 0.7754241\tbest: 0.7755951 (64)\ttotal: 45.7s\tremaining: 4m 40s\n70:\ttest: 0.7750596\tbest: 0.7755951 (64)\ttotal: 46.4s\tremaining: 4m 40s\n71:\ttest: 0.7747357\tbest: 0.7755951 (64)\ttotal: 47s\tremaining: 4m 39s\n72:\ttest: 0.7752441\tbest: 0.7755951 (64)\ttotal: 47.8s\tremaining: 4m 39s\n73:\ttest: 0.7750236\tbest: 0.7755951 (64)\ttotal: 48.4s\tremaining: 4m 38s\n74:\ttest: 0.7748391\tbest: 0.7755951 (64)\ttotal: 49s\tremaining: 4m 37s\n75:\ttest: 0.7746592\tbest: 0.7755951 (64)\ttotal: 49.7s\tremaining: 4m 37s\n76:\ttest: 0.7744297\tbest: 0.7755951 (64)\ttotal: 50.1s\tremaining: 4m 35s\n77:\ttest: 0.7750731\tbest: 0.7755951 (64)\ttotal: 50.9s\tremaining: 4m 35s\n78:\ttest: 0.7752576\tbest: 0.7755951 (64)\ttotal: 51.5s\tremaining: 4m 34s\n79:\ttest: 0.7750461\tbest: 0.7755951 (64)\ttotal: 52.2s\tremaining: 4m 33s\n80:\ttest: 0.7751226\tbest: 0.7755951 (64)\ttotal: 52.8s\tremaining: 4m 32s\n81:\ttest: 0.7751721\tbest: 0.7755951 (64)\ttotal: 53.4s\tremaining: 4m 32s\n82:\ttest: 0.7753251\tbest: 0.7755951 (64)\ttotal: 54.1s\tremaining: 4m 31s\n83:\ttest: 0.7750101\tbest: 0.7755951 (64)\ttotal: 54.8s\tremaining: 4m 31s\n84:\ttest: 0.7750326\tbest: 0.7755951 (64)\ttotal: 55.6s\tremaining: 4m 31s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7755950506\nbestIteration = 64\n\nShrink model to first 65 iterations.\n0:\ttest: 0.6782272\tbest: 0.6782272 (0)\ttotal: 980ms\tremaining: 8m 8s\n1:\ttest: 0.7274646\tbest: 0.7274646 (1)\ttotal: 1.57s\tremaining: 6m 32s\n2:\ttest: 0.7993116\tbest: 0.7993116 (2)\ttotal: 2.28s\tremaining: 6m 18s\n3:\ttest: 0.7961417\tbest: 0.7993116 (2)\ttotal: 3.09s\tremaining: 6m 23s\n4:\ttest: 0.8331901\tbest: 0.8331901 (4)\ttotal: 3.98s\tremaining: 6m 34s\n5:\ttest: 0.8407784\tbest: 0.8407784 (5)\ttotal: 4.87s\tremaining: 6m 41s\n6:\ttest: 0.8380922\tbest: 0.8407784 (5)\ttotal: 5.48s\tremaining: 6m 26s\n7:\ttest: 0.8409314\tbest: 0.8409314 (7)\ttotal: 6.09s\tremaining: 6m 14s\n8:\ttest: 0.8419505\tbest: 0.8419505 (8)\ttotal: 6.68s\tremaining: 6m 4s\n9:\ttest: 0.8421305\tbest: 0.8421305 (9)\ttotal: 7.48s\tremaining: 6m 6s\n10:\ttest: 0.8457098\tbest: 0.8457098 (10)\ttotal: 8.27s\tremaining: 6m 7s\n11:\ttest: 0.8440135\tbest: 0.8457098 (10)\ttotal: 9.19s\tremaining: 6m 13s\n12:\ttest: 0.8437930\tbest: 0.8457098 (10)\ttotal: 10.1s\tremaining: 6m 17s\n13:\ttest: 0.8457638\tbest: 0.8457638 (13)\ttotal: 10.8s\tremaining: 6m 14s\n14:\ttest: 0.8429651\tbest: 0.8457638 (13)\ttotal: 11.6s\tremaining: 6m 14s\n15:\ttest: 0.8469381\tbest: 0.8469381 (15)\ttotal: 12.2s\tremaining: 6m 8s\n16:\ttest: 0.8435456\tbest: 0.8469381 (15)\ttotal: 13.1s\tremaining: 6m 11s\n17:\ttest: 0.8473881\tbest: 0.8473881 (17)\ttotal: 13.8s\tremaining: 6m 8s\n18:\ttest: 0.8489584\tbest: 0.8489584 (18)\ttotal: 14.5s\tremaining: 6m 6s\n19:\ttest: 0.8502767\tbest: 0.8502767 (19)\ttotal: 15.2s\tremaining: 6m 4s\n20:\ttest: 0.8506727\tbest: 0.8506727 (20)\ttotal: 16s\tremaining: 6m 4s\n21:\ttest: 0.8508976\tbest: 0.8508976 (21)\ttotal: 16.8s\tremaining: 6m 4s\n22:\ttest: 0.8518110\tbest: 0.8518110 (22)\ttotal: 17.6s\tremaining: 6m 4s\n23:\ttest: 0.8512576\tbest: 0.8518110 (22)\ttotal: 18.5s\tremaining: 6m 6s\n24:\ttest: 0.8514961\tbest: 0.8518110 (22)\ttotal: 19.3s\tremaining: 6m 6s\n25:\ttest: 0.8517300\tbest: 0.8518110 (22)\ttotal: 20.2s\tremaining: 6m 7s\n26:\ttest: 0.8518740\tbest: 0.8518740 (26)\ttotal: 21.1s\tremaining: 6m 9s\n27:\ttest: 0.8505017\tbest: 0.8518740 (26)\ttotal: 21.8s\tremaining: 6m 7s\n28:\ttest: 0.8503217\tbest: 0.8518740 (26)\ttotal: 22.5s\tremaining: 6m 4s\n29:\ttest: 0.8493948\tbest: 0.8518740 (26)\ttotal: 23.3s\tremaining: 6m 4s\n30:\ttest: 0.8495253\tbest: 0.8518740 (26)\ttotal: 23.9s\tremaining: 6m 1s\n31:\ttest: 0.8490844\tbest: 0.8518740 (26)\ttotal: 24.7s\tremaining: 6m\n32:\ttest: 0.8475546\tbest: 0.8518740 (26)\ttotal: 25.5s\tremaining: 6m\n33:\ttest: 0.8478740\tbest: 0.8518740 (26)\ttotal: 26.3s\tremaining: 6m\n34:\ttest: 0.8473971\tbest: 0.8518740 (26)\ttotal: 27.1s\tremaining: 5m 59s\n35:\ttest: 0.8471316\tbest: 0.8518740 (26)\ttotal: 28s\tremaining: 6m\n36:\ttest: 0.8448279\tbest: 0.8518740 (26)\ttotal: 28.9s\tremaining: 6m 1s\n37:\ttest: 0.8452058\tbest: 0.8518740 (26)\ttotal: 29.9s\tremaining: 6m 3s\n38:\ttest: 0.8454173\tbest: 0.8518740 (26)\ttotal: 30.5s\tremaining: 6m\n39:\ttest: 0.8460202\tbest: 0.8518740 (26)\ttotal: 31.4s\tremaining: 6m\n40:\ttest: 0.8472486\tbest: 0.8518740 (26)\ttotal: 32.1s\tremaining: 5m 59s\n41:\ttest: 0.8474151\tbest: 0.8518740 (26)\ttotal: 32.8s\tremaining: 5m 57s\n42:\ttest: 0.8470821\tbest: 0.8518740 (26)\ttotal: 33.7s\tremaining: 5m 57s\n43:\ttest: 0.8473881\tbest: 0.8518740 (26)\ttotal: 34.5s\tremaining: 5m 57s\n44:\ttest: 0.8468571\tbest: 0.8518740 (26)\ttotal: 35.3s\tremaining: 5m 56s\n45:\ttest: 0.8466367\tbest: 0.8518740 (26)\ttotal: 36.2s\tremaining: 5m 57s\n46:\ttest: 0.8476040\tbest: 0.8518740 (26)\ttotal: 36.9s\tremaining: 5m 55s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8518740157\nbestIteration = 26\n\nShrink model to first 27 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 992ms\tremaining: 8m 14s\n1:\ttest: 0.7499280\tbest: 0.7499280 (1)\ttotal: 1.6s\tremaining: 6m 37s\n2:\ttest: 0.7915883\tbest: 0.7915883 (2)\ttotal: 2.3s\tremaining: 6m 21s\n3:\ttest: 0.7909921\tbest: 0.7915883 (2)\ttotal: 3.1s\tremaining: 6m 24s\n4:\ttest: 0.7998065\tbest: 0.7998065 (4)\ttotal: 3.79s\tremaining: 6m 15s\n5:\ttest: 0.8108684\tbest: 0.8108684 (5)\ttotal: 4.59s\tremaining: 6m 18s\n6:\ttest: 0.8460832\tbest: 0.8460832 (6)\ttotal: 5.3s\tremaining: 6m 13s\n7:\ttest: 0.8471676\tbest: 0.8471676 (7)\ttotal: 6.1s\tremaining: 6m 14s\n8:\ttest: 0.8524409\tbest: 0.8524409 (8)\ttotal: 6.89s\tremaining: 6m 16s\n9:\ttest: 0.8476895\tbest: 0.8524409 (8)\ttotal: 7.59s\tremaining: 6m 11s\n10:\ttest: 0.8430461\tbest: 0.8524409 (8)\ttotal: 8.39s\tremaining: 6m 12s\n11:\ttest: 0.8413318\tbest: 0.8524409 (8)\ttotal: 9.29s\tremaining: 6m 17s\n12:\ttest: 0.8436535\tbest: 0.8524409 (8)\ttotal: 9.89s\tremaining: 6m 10s\n13:\ttest: 0.8384522\tbest: 0.8524409 (8)\ttotal: 10.7s\tremaining: 6m 11s\n14:\ttest: 0.8404589\tbest: 0.8524409 (8)\ttotal: 11.4s\tremaining: 6m 8s\n15:\ttest: 0.8410259\tbest: 0.8524409 (8)\ttotal: 12.2s\tremaining: 6m 9s\n16:\ttest: 0.8404319\tbest: 0.8524409 (8)\ttotal: 13.1s\tremaining: 6m 12s\n17:\ttest: 0.8426997\tbest: 0.8524409 (8)\ttotal: 14s\tremaining: 6m 14s\n18:\ttest: 0.8444274\tbest: 0.8524409 (8)\ttotal: 14.8s\tremaining: 6m 14s\n19:\ttest: 0.8490079\tbest: 0.8524409 (8)\ttotal: 15.7s\tremaining: 6m 16s\n20:\ttest: 0.8471946\tbest: 0.8524409 (8)\ttotal: 16.5s\tremaining: 6m 16s\n21:\ttest: 0.8464297\tbest: 0.8524409 (8)\ttotal: 17.1s\tremaining: 6m 11s\n22:\ttest: 0.8466232\tbest: 0.8524409 (8)\ttotal: 17.9s\tremaining: 6m 10s\n23:\ttest: 0.8461507\tbest: 0.8524409 (8)\ttotal: 18.5s\tremaining: 6m 6s\n24:\ttest: 0.8448864\tbest: 0.8524409 (8)\ttotal: 19.2s\tremaining: 6m 4s\n25:\ttest: 0.8436445\tbest: 0.8524409 (8)\ttotal: 19.9s\tremaining: 6m 2s\n26:\ttest: 0.8446164\tbest: 0.8524409 (8)\ttotal: 20.7s\tremaining: 6m 2s\n27:\ttest: 0.8447109\tbest: 0.8524409 (8)\ttotal: 21.5s\tremaining: 6m 2s\n28:\ttest: 0.8445579\tbest: 0.8524409 (8)\ttotal: 22.2s\tremaining: 6m\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8524409449\nbestIteration = 8\n\nShrink model to first 9 iterations.\n0:\ttest: 0.6539235\tbest: 0.6539235 (0)\ttotal: 793ms\tremaining: 6m 35s\n1:\ttest: 0.6772891\tbest: 0.6772891 (1)\ttotal: 1.6s\tremaining: 6m 37s\n2:\ttest: 0.6700945\tbest: 0.6772891 (1)\ttotal: 2.5s\tremaining: 6m 54s\n3:\ttest: 0.6647357\tbest: 0.6772891 (1)\ttotal: 3.3s\tremaining: 6m 49s\n4:\ttest: 0.7304882\tbest: 0.7304882 (4)\ttotal: 4.02s\tremaining: 6m 37s\n5:\ttest: 0.7311586\tbest: 0.7311586 (5)\ttotal: 4.81s\tremaining: 6m 36s\n6:\ttest: 0.7395321\tbest: 0.7395321 (6)\ttotal: 5.51s\tremaining: 6m 27s\n7:\ttest: 0.7426097\tbest: 0.7426097 (7)\ttotal: 6.41s\tremaining: 6m 34s\n8:\ttest: 0.7534466\tbest: 0.7534466 (8)\ttotal: 7.11s\tremaining: 6m 27s\n9:\ttest: 0.7572396\tbest: 0.7572396 (9)\ttotal: 7.81s\tremaining: 6m 22s\n10:\ttest: 0.7587514\tbest: 0.7587514 (10)\ttotal: 8.41s\tremaining: 6m 13s\n11:\ttest: 0.7580180\tbest: 0.7587514 (10)\ttotal: 9.21s\tremaining: 6m 14s\n12:\ttest: 0.7579460\tbest: 0.7587514 (10)\ttotal: 9.81s\tremaining: 6m 7s\n13:\ttest: 0.7590911\tbest: 0.7590911 (13)\ttotal: 10.3s\tremaining: 5m 57s\n14:\ttest: 0.7587942\tbest: 0.7590911 (13)\ttotal: 10.9s\tremaining: 5m 52s\n15:\ttest: 0.7576040\tbest: 0.7590911 (13)\ttotal: 11.5s\tremaining: 5m 48s\n16:\ttest: 0.7589606\tbest: 0.7590911 (13)\ttotal: 12.2s\tremaining: 5m 46s\n17:\ttest: 0.7604049\tbest: 0.7604049 (17)\ttotal: 12.8s\tremaining: 5m 43s\n18:\ttest: 0.7597120\tbest: 0.7604049 (17)\ttotal: 13.6s\tremaining: 5m 44s\n19:\ttest: 0.7597525\tbest: 0.7604049 (17)\ttotal: 14.4s\tremaining: 5m 46s\n20:\ttest: 0.7598740\tbest: 0.7604049 (17)\ttotal: 15.2s\tremaining: 5m 47s\n21:\ttest: 0.7628391\tbest: 0.7628391 (21)\ttotal: 16s\tremaining: 5m 48s\n22:\ttest: 0.7623712\tbest: 0.7628391 (21)\ttotal: 16.8s\tremaining: 5m 48s\n23:\ttest: 0.7627897\tbest: 0.7628391 (21)\ttotal: 17.3s\tremaining: 5m 43s\n24:\ttest: 0.7625152\tbest: 0.7628391 (21)\ttotal: 18s\tremaining: 5m 42s\n25:\ttest: 0.7622722\tbest: 0.7628391 (21)\ttotal: 18.7s\tremaining: 5m 41s\n26:\ttest: 0.7625062\tbest: 0.7628391 (21)\ttotal: 19.5s\tremaining: 5m 41s\n27:\ttest: 0.7626592\tbest: 0.7628391 (21)\ttotal: 20.6s\tremaining: 5m 47s\n28:\ttest: 0.7626772\tbest: 0.7628391 (21)\ttotal: 21.2s\tremaining: 5m 44s\n29:\ttest: 0.7642880\tbest: 0.7642880 (29)\ttotal: 22.2s\tremaining: 5m 47s\n30:\ttest: 0.7647244\tbest: 0.7647244 (30)\ttotal: 23.1s\tremaining: 5m 49s\n31:\ttest: 0.7649584\tbest: 0.7649584 (31)\ttotal: 24.2s\tremaining: 5m 54s\n32:\ttest: 0.7641035\tbest: 0.7649584 (31)\ttotal: 25s\tremaining: 5m 53s\n33:\ttest: 0.7658988\tbest: 0.7658988 (33)\ttotal: 25.9s\tremaining: 5m 55s\n34:\ttest: 0.7670101\tbest: 0.7670101 (34)\ttotal: 26.9s\tremaining: 5m 57s\n35:\ttest: 0.7667492\tbest: 0.7670101 (34)\ttotal: 27.8s\tremaining: 5m 58s\n36:\ttest: 0.7662272\tbest: 0.7670101 (34)\ttotal: 28.3s\tremaining: 5m 54s\n37:\ttest: 0.7663802\tbest: 0.7670101 (34)\ttotal: 29.4s\tremaining: 5m 57s\n38:\ttest: 0.7668346\tbest: 0.7670101 (34)\ttotal: 30.1s\tremaining: 5m 55s\n39:\ttest: 0.7667852\tbest: 0.7670101 (34)\ttotal: 30.7s\tremaining: 5m 53s\n40:\ttest: 0.7681260\tbest: 0.7681260 (40)\ttotal: 31.4s\tremaining: 5m 51s\n41:\ttest: 0.7689764\tbest: 0.7689764 (41)\ttotal: 32.2s\tremaining: 5m 51s\n42:\ttest: 0.7685714\tbest: 0.7689764 (41)\ttotal: 32.9s\tremaining: 5m 49s\n43:\ttest: 0.7676265\tbest: 0.7689764 (41)\ttotal: 33.9s\tremaining: 5m 51s\n44:\ttest: 0.7671856\tbest: 0.7689764 (41)\ttotal: 34.8s\tremaining: 5m 51s\n45:\ttest: 0.7659438\tbest: 0.7689764 (41)\ttotal: 35.7s\tremaining: 5m 52s\n46:\ttest: 0.7663712\tbest: 0.7689764 (41)\ttotal: 36.7s\tremaining: 5m 53s\n47:\ttest: 0.7661822\tbest: 0.7689764 (41)\ttotal: 37.7s\tremaining: 5m 55s\n48:\ttest: 0.7654398\tbest: 0.7689764 (41)\ttotal: 38.6s\tremaining: 5m 55s\n49:\ttest: 0.7651969\tbest: 0.7689764 (41)\ttotal: 39.6s\tremaining: 5m 56s\n50:\ttest: 0.7650934\tbest: 0.7689764 (41)\ttotal: 40.5s\tremaining: 5m 56s\n51:\ttest: 0.7644994\tbest: 0.7689764 (41)\ttotal: 41.3s\tremaining: 5m 55s\n52:\ttest: 0.7641980\tbest: 0.7689764 (41)\ttotal: 42.3s\tremaining: 5m 56s\n53:\ttest: 0.7650529\tbest: 0.7689764 (41)\ttotal: 43.2s\tremaining: 5m 56s\n54:\ttest: 0.7645489\tbest: 0.7689764 (41)\ttotal: 44.3s\tremaining: 5m 58s\n55:\ttest: 0.7648504\tbest: 0.7689764 (41)\ttotal: 45.3s\tremaining: 5m 59s\n56:\ttest: 0.7647604\tbest: 0.7689764 (41)\ttotal: 46.3s\tremaining: 5m 59s\n57:\ttest: 0.7643735\tbest: 0.7689764 (41)\ttotal: 47s\tremaining: 5m 58s\n58:\ttest: 0.7649989\tbest: 0.7689764 (41)\ttotal: 48s\tremaining: 5m 58s\n59:\ttest: 0.7662092\tbest: 0.7689764 (41)\ttotal: 49s\tremaining: 5m 59s\n60:\ttest: 0.7657188\tbest: 0.7689764 (41)\ttotal: 49.9s\tremaining: 5m 59s\n61:\ttest: 0.7661597\tbest: 0.7689764 (41)\ttotal: 50.7s\tremaining: 5m 58s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.768976378\nbestIteration = 41\n\nShrink model to first 42 iterations.\n0:\ttest: 0.6782272\tbest: 0.6782272 (0)\ttotal: 987ms\tremaining: 8m 12s\n1:\ttest: 0.7274781\tbest: 0.7274781 (1)\ttotal: 1.79s\tremaining: 7m 26s\n2:\ttest: 0.7967244\tbest: 0.7967244 (2)\ttotal: 2.49s\tremaining: 6m 52s\n3:\ttest: 0.7994443\tbest: 0.7994443 (3)\ttotal: 3.19s\tremaining: 6m 35s\n4:\ttest: 0.8381035\tbest: 0.8381035 (4)\ttotal: 3.69s\tremaining: 6m 5s\n5:\ttest: 0.8501080\tbest: 0.8501080 (5)\ttotal: 4.59s\tremaining: 6m 17s\n6:\ttest: 0.8279190\tbest: 0.8501080 (5)\ttotal: 5.49s\tremaining: 6m 26s\n7:\ttest: 0.8311159\tbest: 0.8501080 (5)\ttotal: 6.09s\tremaining: 6m 14s\n8:\ttest: 0.8303240\tbest: 0.8501080 (5)\ttotal: 6.79s\tremaining: 6m 10s\n9:\ttest: 0.8341912\tbest: 0.8501080 (5)\ttotal: 7.59s\tremaining: 6m 11s\n10:\ttest: 0.8368639\tbest: 0.8501080 (5)\ttotal: 8.58s\tremaining: 6m 21s\n11:\ttest: 0.8337053\tbest: 0.8501080 (5)\ttotal: 9.49s\tremaining: 6m 25s\n12:\ttest: 0.8349741\tbest: 0.8501080 (5)\ttotal: 10.1s\tremaining: 6m 18s\n13:\ttest: 0.8355456\tbest: 0.8501080 (5)\ttotal: 10.9s\tremaining: 6m 18s\n14:\ttest: 0.8362565\tbest: 0.8501080 (5)\ttotal: 11.6s\tremaining: 6m 14s\n15:\ttest: 0.8341732\tbest: 0.8501080 (5)\ttotal: 12.5s\tremaining: 6m 17s\n16:\ttest: 0.8330259\tbest: 0.8501080 (5)\ttotal: 13.3s\tremaining: 6m 17s\n17:\ttest: 0.8305062\tbest: 0.8501080 (5)\ttotal: 14.1s\tremaining: 6m 17s\n18:\ttest: 0.8311766\tbest: 0.8501080 (5)\ttotal: 15s\tremaining: 6m 19s\n19:\ttest: 0.8311046\tbest: 0.8501080 (5)\ttotal: 15.9s\tremaining: 6m 21s\n20:\ttest: 0.8277165\tbest: 0.8501080 (5)\ttotal: 16.7s\tremaining: 6m 20s\n21:\ttest: 0.8269786\tbest: 0.8501080 (5)\ttotal: 17.3s\tremaining: 6m 15s\n22:\ttest: 0.8274601\tbest: 0.8501080 (5)\ttotal: 18.1s\tremaining: 6m 15s\n23:\ttest: 0.8270641\tbest: 0.8501080 (5)\ttotal: 18.9s\tremaining: 6m 14s\n24:\ttest: 0.8267897\tbest: 0.8501080 (5)\ttotal: 19.8s\tremaining: 6m 15s\n25:\ttest: 0.8260967\tbest: 0.8501080 (5)\ttotal: 20.7s\tremaining: 6m 17s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8501079865\nbestIteration = 5\n\nShrink model to first 6 iterations.\n0:\ttest: 0.6859528\tbest: 0.6859528 (0)\ttotal: 992ms\tremaining: 8m 14s\n1:\ttest: 0.8092103\tbest: 0.8092103 (1)\ttotal: 1.79s\tremaining: 7m 25s\n2:\ttest: 0.8224139\tbest: 0.8224139 (2)\ttotal: 2.7s\tremaining: 7m 26s\n3:\ttest: 0.8226457\tbest: 0.8226457 (3)\ttotal: 3.69s\tremaining: 7m 37s\n4:\ttest: 0.8279438\tbest: 0.8279438 (4)\ttotal: 4.5s\tremaining: 7m 25s\n5:\ttest: 0.8289494\tbest: 0.8289494 (5)\ttotal: 5.59s\tremaining: 7m 40s\n6:\ttest: 0.8333498\tbest: 0.8333498 (6)\ttotal: 6.39s\tremaining: 7m 30s\n7:\ttest: 0.8220540\tbest: 0.8333498 (6)\ttotal: 7.49s\tremaining: 7m 40s\n8:\ttest: 0.8283420\tbest: 0.8333498 (6)\ttotal: 8.49s\tremaining: 7m 43s\n9:\ttest: 0.8188031\tbest: 0.8333498 (6)\ttotal: 9.3s\tremaining: 7m 35s\n10:\ttest: 0.8270529\tbest: 0.8333498 (6)\ttotal: 10.4s\tremaining: 7m 42s\n11:\ttest: 0.8309494\tbest: 0.8333498 (6)\ttotal: 11.2s\tremaining: 7m 35s\n12:\ttest: 0.8290326\tbest: 0.8333498 (6)\ttotal: 12.1s\tremaining: 7m 33s\n13:\ttest: 0.8308886\tbest: 0.8333498 (6)\ttotal: 12.9s\tremaining: 7m 28s\n14:\ttest: 0.8315411\tbest: 0.8333498 (6)\ttotal: 13.8s\tremaining: 7m 26s\n15:\ttest: 0.8306727\tbest: 0.8333498 (6)\ttotal: 14.7s\tremaining: 7m 24s\n16:\ttest: 0.8325849\tbest: 0.8333498 (6)\ttotal: 15.8s\tremaining: 7m 29s\n17:\ttest: 0.8339978\tbest: 0.8339978 (17)\ttotal: 16.8s\tremaining: 7m 29s\n18:\ttest: 0.8348796\tbest: 0.8348796 (18)\ttotal: 17.5s\tremaining: 7m 23s\n19:\ttest: 0.8348976\tbest: 0.8348976 (19)\ttotal: 18.4s\tremaining: 7m 21s\n20:\ttest: 0.8351991\tbest: 0.8351991 (20)\ttotal: 19.3s\tremaining: 7m 20s\n21:\ttest: 0.8356670\tbest: 0.8356670 (21)\ttotal: 20.3s\tremaining: 7m 21s\n22:\ttest: 0.8357075\tbest: 0.8357075 (22)\ttotal: 21.2s\tremaining: 7m 19s\n23:\ttest: 0.8379618\tbest: 0.8379618 (23)\ttotal: 22.1s\tremaining: 7m 18s\n24:\ttest: 0.8374983\tbest: 0.8379618 (23)\ttotal: 22.8s\tremaining: 7m 13s\n25:\ttest: 0.8388931\tbest: 0.8388931 (25)\ttotal: 23.6s\tremaining: 7m 10s\n26:\ttest: 0.8394826\tbest: 0.8394826 (26)\ttotal: 24.3s\tremaining: 7m 5s\n27:\ttest: 0.8377548\tbest: 0.8394826 (26)\ttotal: 25.2s\tremaining: 7m 4s\n28:\ttest: 0.8390011\tbest: 0.8394826 (26)\ttotal: 25.9s\tremaining: 7m\n29:\ttest: 0.8384117\tbest: 0.8394826 (26)\ttotal: 26.7s\tremaining: 6m 58s\n30:\ttest: 0.8366524\tbest: 0.8394826 (26)\ttotal: 27.5s\tremaining: 6m 56s\n31:\ttest: 0.8377188\tbest: 0.8394826 (26)\ttotal: 28.5s\tremaining: 6m 56s\n32:\ttest: 0.8391901\tbest: 0.8394826 (26)\ttotal: 29.4s\tremaining: 6m 55s\n33:\ttest: 0.8391631\tbest: 0.8394826 (26)\ttotal: 30.2s\tremaining: 6m 54s\n34:\ttest: 0.8402475\tbest: 0.8402475 (34)\ttotal: 31.1s\tremaining: 6m 53s\n35:\ttest: 0.8375748\tbest: 0.8402475 (34)\ttotal: 31.9s\tremaining: 6m 51s\n36:\ttest: 0.8359730\tbest: 0.8402475 (34)\ttotal: 32.8s\tremaining: 6m 50s\n37:\ttest: 0.8355456\tbest: 0.8402475 (34)\ttotal: 33.9s\tremaining: 6m 52s\n38:\ttest: 0.8348841\tbest: 0.8402475 (34)\ttotal: 34.7s\tremaining: 6m 50s\n39:\ttest: 0.8349021\tbest: 0.8402475 (34)\ttotal: 35.4s\tremaining: 6m 47s\n40:\ttest: 0.8348661\tbest: 0.8402475 (34)\ttotal: 36.3s\tremaining: 6m 46s\n41:\ttest: 0.8353431\tbest: 0.8402475 (34)\ttotal: 37.3s\tremaining: 6m 46s\n42:\ttest: 0.8356220\tbest: 0.8402475 (34)\ttotal: 38s\tremaining: 6m 43s\n43:\ttest: 0.8350731\tbest: 0.8402475 (34)\ttotal: 38.8s\tremaining: 6m 42s\n44:\ttest: 0.8342857\tbest: 0.8402475 (34)\ttotal: 39.7s\tremaining: 6m 41s\n45:\ttest: 0.8341012\tbest: 0.8402475 (34)\ttotal: 40.8s\tremaining: 6m 42s\n46:\ttest: 0.8344567\tbest: 0.8402475 (34)\ttotal: 41.6s\tremaining: 6m 40s\n47:\ttest: 0.8341147\tbest: 0.8402475 (34)\ttotal: 42.5s\tremaining: 6m 40s\n48:\ttest: 0.8341102\tbest: 0.8402475 (34)\ttotal: 43.3s\tremaining: 6m 38s\n49:\ttest: 0.8342992\tbest: 0.8402475 (34)\ttotal: 43.9s\tremaining: 6m 35s\n50:\ttest: 0.8345512\tbest: 0.8402475 (34)\ttotal: 44.9s\tremaining: 6m 35s\n51:\ttest: 0.8345422\tbest: 0.8402475 (34)\ttotal: 45.7s\tremaining: 6m 33s\n52:\ttest: 0.8351676\tbest: 0.8402475 (34)\ttotal: 46.8s\tremaining: 6m 34s\n53:\ttest: 0.8348256\tbest: 0.8402475 (34)\ttotal: 47.8s\tremaining: 6m 34s\n54:\ttest: 0.8354916\tbest: 0.8402475 (34)\ttotal: 48.7s\tremaining: 6m 33s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8402474691\nbestIteration = 34\n\nShrink model to first 35 iterations.\n0:\ttest: 0.6722835\tbest: 0.6722835 (0)\ttotal: 1.1s\tremaining: 9m 10s\n1:\ttest: 0.7178920\tbest: 0.7178920 (1)\ttotal: 1.91s\tremaining: 7m 54s\n2:\ttest: 0.7303060\tbest: 0.7303060 (2)\ttotal: 3s\tremaining: 8m 17s\n3:\ttest: 0.7529651\tbest: 0.7529651 (3)\ttotal: 4.01s\tremaining: 8m 16s\n4:\ttest: 0.7587109\tbest: 0.7587109 (4)\ttotal: 4.9s\tremaining: 8m 5s\n5:\ttest: 0.7584522\tbest: 0.7587109 (4)\ttotal: 6s\tremaining: 8m 13s\n6:\ttest: 0.7560810\tbest: 0.7587109 (4)\ttotal: 6.91s\tremaining: 8m 6s\n7:\ttest: 0.7604814\tbest: 0.7604814 (7)\ttotal: 7.7s\tremaining: 7m 53s\n8:\ttest: 0.7629854\tbest: 0.7629854 (8)\ttotal: 8.5s\tremaining: 7m 43s\n9:\ttest: 0.7618920\tbest: 0.7629854 (8)\ttotal: 9.4s\tremaining: 7m 40s\n10:\ttest: 0.7610394\tbest: 0.7629854 (8)\ttotal: 10.1s\tremaining: 7m 28s\n11:\ttest: 0.7617165\tbest: 0.7629854 (8)\ttotal: 11.1s\tremaining: 7m 31s\n12:\ttest: 0.7637413\tbest: 0.7637413 (12)\ttotal: 12s\tremaining: 7m 29s\n13:\ttest: 0.7608346\tbest: 0.7637413 (12)\ttotal: 12.9s\tremaining: 7m 27s\n14:\ttest: 0.7610281\tbest: 0.7637413 (12)\ttotal: 13.7s\tremaining: 7m 22s\n15:\ttest: 0.7605557\tbest: 0.7637413 (12)\ttotal: 14.5s\tremaining: 7m 18s\n16:\ttest: 0.7624882\tbest: 0.7637413 (12)\ttotal: 15.5s\tremaining: 7m 20s\n17:\ttest: 0.7635591\tbest: 0.7637413 (12)\ttotal: 16.4s\tremaining: 7m 19s\n18:\ttest: 0.7625242\tbest: 0.7637413 (12)\ttotal: 17.2s\tremaining: 7m 15s\n19:\ttest: 0.7631541\tbest: 0.7637413 (12)\ttotal: 18.1s\tremaining: 7m 14s\n20:\ttest: 0.7619933\tbest: 0.7637413 (12)\ttotal: 19s\tremaining: 7m 13s\n21:\ttest: 0.7621237\tbest: 0.7637413 (12)\ttotal: 20s\tremaining: 7m 14s\n22:\ttest: 0.7655703\tbest: 0.7655703 (22)\ttotal: 20.8s\tremaining: 7m 11s\n23:\ttest: 0.7654938\tbest: 0.7655703 (22)\ttotal: 21.6s\tremaining: 7m 8s\n24:\ttest: 0.7649224\tbest: 0.7655703 (22)\ttotal: 22.7s\tremaining: 7m 11s\n25:\ttest: 0.7650484\tbest: 0.7655703 (22)\ttotal: 23.6s\tremaining: 7m 10s\n26:\ttest: 0.7654848\tbest: 0.7655703 (22)\ttotal: 24.4s\tremaining: 7m 7s\n27:\ttest: 0.7653633\tbest: 0.7655703 (22)\ttotal: 25.2s\tremaining: 7m 4s\n28:\ttest: 0.7660112\tbest: 0.7660112 (28)\ttotal: 26.1s\tremaining: 7m 3s\n29:\ttest: 0.7665422\tbest: 0.7665422 (29)\ttotal: 27.1s\tremaining: 7m 4s\n30:\ttest: 0.7661777\tbest: 0.7665422 (29)\ttotal: 28s\tremaining: 7m 3s\n31:\ttest: 0.7645759\tbest: 0.7665422 (29)\ttotal: 28.6s\tremaining: 6m 58s\n32:\ttest: 0.7638785\tbest: 0.7665422 (29)\ttotal: 29.5s\tremaining: 6m 57s\n33:\ttest: 0.7643060\tbest: 0.7665422 (29)\ttotal: 30.5s\tremaining: 6m 57s\n34:\ttest: 0.7646119\tbest: 0.7665422 (29)\ttotal: 31.3s\tremaining: 6m 55s\n35:\ttest: 0.7654668\tbest: 0.7665422 (29)\ttotal: 32.2s\tremaining: 6m 55s\n36:\ttest: 0.7656738\tbest: 0.7665422 (29)\ttotal: 33.1s\tremaining: 6m 54s\n37:\ttest: 0.7657098\tbest: 0.7665422 (29)\ttotal: 34.1s\tremaining: 6m 54s\n38:\ttest: 0.7662812\tbest: 0.7665422 (29)\ttotal: 35.2s\tremaining: 6m 55s\n39:\ttest: 0.7664027\tbest: 0.7665422 (29)\ttotal: 36.2s\tremaining: 6m 56s\n40:\ttest: 0.7675276\tbest: 0.7675276 (40)\ttotal: 37.1s\tremaining: 6m 55s\n41:\ttest: 0.7685219\tbest: 0.7685219 (41)\ttotal: 38.1s\tremaining: 6m 55s\n42:\ttest: 0.7694938\tbest: 0.7694938 (42)\ttotal: 39.1s\tremaining: 6m 55s\n43:\ttest: 0.7708436\tbest: 0.7708436 (43)\ttotal: 40s\tremaining: 6m 54s\n44:\ttest: 0.7711361\tbest: 0.7711361 (44)\ttotal: 41.1s\tremaining: 6m 55s\n45:\ttest: 0.7700472\tbest: 0.7711361 (44)\ttotal: 42s\tremaining: 6m 54s\n46:\ttest: 0.7697953\tbest: 0.7711361 (44)\ttotal: 43s\tremaining: 6m 54s\n47:\ttest: 0.7693003\tbest: 0.7711361 (44)\ttotal: 44s\tremaining: 6m 54s\n48:\ttest: 0.7700472\tbest: 0.7711361 (44)\ttotal: 44.8s\tremaining: 6m 52s\n49:\ttest: 0.7702497\tbest: 0.7711361 (44)\ttotal: 45.6s\tremaining: 6m 50s\n50:\ttest: 0.7705017\tbest: 0.7711361 (44)\ttotal: 46.4s\tremaining: 6m 48s\n51:\ttest: 0.7702272\tbest: 0.7711361 (44)\ttotal: 47.4s\tremaining: 6m 48s\n52:\ttest: 0.7701777\tbest: 0.7711361 (44)\ttotal: 48.2s\tremaining: 6m 46s\n53:\ttest: 0.7702677\tbest: 0.7711361 (44)\ttotal: 49.1s\tremaining: 6m 45s\n54:\ttest: 0.7698853\tbest: 0.7711361 (44)\ttotal: 49.9s\tremaining: 6m 43s\n55:\ttest: 0.7710191\tbest: 0.7711361 (44)\ttotal: 50.8s\tremaining: 6m 42s\n56:\ttest: 0.7707447\tbest: 0.7711361 (44)\ttotal: 51.9s\tremaining: 6m 43s\n57:\ttest: 0.7706772\tbest: 0.7711361 (44)\ttotal: 52.9s\tremaining: 6m 43s\n58:\ttest: 0.7701057\tbest: 0.7711361 (44)\ttotal: 53.8s\tremaining: 6m 42s\n59:\ttest: 0.7701597\tbest: 0.7711361 (44)\ttotal: 54.5s\tremaining: 6m 39s\n60:\ttest: 0.7710821\tbest: 0.7711361 (44)\ttotal: 55.6s\tremaining: 6m 40s\n61:\ttest: 0.7710866\tbest: 0.7711361 (44)\ttotal: 56.2s\tremaining: 6m 37s\n62:\ttest: 0.7712261\tbest: 0.7712261 (62)\ttotal: 56.8s\tremaining: 6m 33s\n63:\ttest: 0.7709336\tbest: 0.7712261 (62)\ttotal: 57.7s\tremaining: 6m 33s\n64:\ttest: 0.7710371\tbest: 0.7712261 (62)\ttotal: 58.6s\tremaining: 6m 32s\n65:\ttest: 0.7715231\tbest: 0.7715231 (65)\ttotal: 59.3s\tremaining: 6m 29s\n66:\ttest: 0.7719775\tbest: 0.7719775 (66)\ttotal: 1m\tremaining: 6m 28s\n67:\ttest: 0.7721710\tbest: 0.7721710 (67)\ttotal: 1m 1s\tremaining: 6m 27s\n68:\ttest: 0.7718245\tbest: 0.7721710 (67)\ttotal: 1m 1s\tremaining: 6m 26s\n69:\ttest: 0.7722880\tbest: 0.7722880 (69)\ttotal: 1m 2s\tremaining: 6m 26s\n70:\ttest: 0.7724184\tbest: 0.7724184 (70)\ttotal: 1m 3s\tremaining: 6m 26s\n71:\ttest: 0.7711496\tbest: 0.7724184 (70)\ttotal: 1m 5s\tremaining: 6m 27s\n72:\ttest: 0.7706997\tbest: 0.7724184 (70)\ttotal: 1m 6s\tremaining: 6m 27s\n73:\ttest: 0.7706412\tbest: 0.7724184 (70)\ttotal: 1m 7s\tremaining: 6m 26s\n74:\ttest: 0.7704297\tbest: 0.7724184 (70)\ttotal: 1m 7s\tremaining: 6m 24s\n75:\ttest: 0.7702362\tbest: 0.7724184 (70)\ttotal: 1m 8s\tremaining: 6m 22s\n76:\ttest: 0.7703937\tbest: 0.7724184 (70)\ttotal: 1m 9s\tremaining: 6m 22s\n77:\ttest: 0.7699078\tbest: 0.7724184 (70)\ttotal: 1m 10s\tremaining: 6m 22s\n78:\ttest: 0.7701912\tbest: 0.7724184 (70)\ttotal: 1m 11s\tremaining: 6m 21s\n79:\ttest: 0.7702992\tbest: 0.7724184 (70)\ttotal: 1m 12s\tremaining: 6m 19s\n80:\ttest: 0.7706457\tbest: 0.7724184 (70)\ttotal: 1m 13s\tremaining: 6m 18s\n81:\ttest: 0.7707132\tbest: 0.7724184 (70)\ttotal: 1m 14s\tremaining: 6m 17s\n82:\ttest: 0.7701642\tbest: 0.7724184 (70)\ttotal: 1m 14s\tremaining: 6m 16s\n83:\ttest: 0.7701822\tbest: 0.7724184 (70)\ttotal: 1m 16s\tremaining: 6m 16s\n84:\ttest: 0.7698808\tbest: 0.7724184 (70)\ttotal: 1m 17s\tremaining: 6m 16s\n85:\ttest: 0.7700742\tbest: 0.7724184 (70)\ttotal: 1m 18s\tremaining: 6m 16s\n86:\ttest: 0.7698493\tbest: 0.7724184 (70)\ttotal: 1m 19s\tremaining: 6m 15s\n87:\ttest: 0.7697188\tbest: 0.7724184 (70)\ttotal: 1m 20s\tremaining: 6m 14s\n88:\ttest: 0.7695748\tbest: 0.7724184 (70)\ttotal: 1m 21s\tremaining: 6m 15s\n89:\ttest: 0.7695433\tbest: 0.7724184 (70)\ttotal: 1m 22s\tremaining: 6m 14s\n90:\ttest: 0.7692148\tbest: 0.7724184 (70)\ttotal: 1m 23s\tremaining: 6m 13s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7724184477\nbestIteration = 70\n\nShrink model to first 71 iterations.\n0:\ttest: 0.6802745\tbest: 0.6802745 (0)\ttotal: 881ms\tremaining: 7m 19s\n1:\ttest: 0.7564949\tbest: 0.7564949 (1)\ttotal: 1.68s\tremaining: 6m 58s\n2:\ttest: 0.7661710\tbest: 0.7661710 (2)\ttotal: 2.49s\tremaining: 6m 52s\n3:\ttest: 0.7545039\tbest: 0.7661710 (2)\ttotal: 3.38s\tremaining: 6m 59s\n4:\ttest: 0.8389651\tbest: 0.8389651 (4)\ttotal: 4.19s\tremaining: 6m 54s\n5:\ttest: 0.8405714\tbest: 0.8405714 (5)\ttotal: 4.88s\tremaining: 6m 41s\n6:\ttest: 0.8402835\tbest: 0.8405714 (5)\ttotal: 5.78s\tremaining: 6m 46s\n7:\ttest: 0.8405309\tbest: 0.8405714 (5)\ttotal: 6.48s\tremaining: 6m 38s\n8:\ttest: 0.8422970\tbest: 0.8422970 (8)\ttotal: 7.49s\tremaining: 6m 48s\n9:\ttest: 0.8410214\tbest: 0.8422970 (8)\ttotal: 8.48s\tremaining: 6m 55s\n10:\ttest: 0.8389111\tbest: 0.8422970 (8)\ttotal: 9.29s\tremaining: 6m 52s\n11:\ttest: 0.8455973\tbest: 0.8455973 (11)\ttotal: 10.2s\tremaining: 6m 54s\n12:\ttest: 0.8471676\tbest: 0.8471676 (12)\ttotal: 11.1s\tremaining: 6m 55s\n13:\ttest: 0.8486524\tbest: 0.8486524 (13)\ttotal: 12s\tremaining: 6m 55s\n14:\ttest: 0.8486164\tbest: 0.8486524 (13)\ttotal: 12.8s\tremaining: 6m 53s\n15:\ttest: 0.8498358\tbest: 0.8498358 (15)\ttotal: 13.6s\tremaining: 6m 50s\n16:\ttest: 0.8524274\tbest: 0.8524274 (16)\ttotal: 14.2s\tremaining: 6m 42s\n17:\ttest: 0.8540202\tbest: 0.8540202 (17)\ttotal: 15s\tremaining: 6m 41s\n18:\ttest: 0.8533138\tbest: 0.8540202 (17)\ttotal: 15.9s\tremaining: 6m 41s\n19:\ttest: 0.8546682\tbest: 0.8546682 (19)\ttotal: 16.7s\tremaining: 6m 40s\n20:\ttest: 0.8542947\tbest: 0.8546682 (19)\ttotal: 17.5s\tremaining: 6m 38s\n21:\ttest: 0.8547447\tbest: 0.8547447 (21)\ttotal: 18.4s\tremaining: 6m 39s\n22:\ttest: 0.8500427\tbest: 0.8547447 (21)\ttotal: 19s\tremaining: 6m 33s\n23:\ttest: 0.8493048\tbest: 0.8547447 (21)\ttotal: 19.9s\tremaining: 6m 34s\n24:\ttest: 0.8483735\tbest: 0.8547447 (21)\ttotal: 20.6s\tremaining: 6m 30s\n25:\ttest: 0.8488234\tbest: 0.8547447 (21)\ttotal: 21.3s\tremaining: 6m 27s\n26:\ttest: 0.8492328\tbest: 0.8547447 (21)\ttotal: 22.1s\tremaining: 6m 26s\n27:\ttest: 0.8483555\tbest: 0.8547447 (21)\ttotal: 23s\tremaining: 6m 27s\n28:\ttest: 0.8488369\tbest: 0.8547447 (21)\ttotal: 23.8s\tremaining: 6m 26s\n29:\ttest: 0.8452148\tbest: 0.8547447 (21)\ttotal: 24.6s\tremaining: 6m 24s\n30:\ttest: 0.8442745\tbest: 0.8547447 (21)\ttotal: 25.3s\tremaining: 6m 22s\n31:\ttest: 0.8436130\tbest: 0.8547447 (21)\ttotal: 26.1s\tremaining: 6m 21s\n32:\ttest: 0.8445039\tbest: 0.8547447 (21)\ttotal: 27s\tremaining: 6m 21s\n33:\ttest: 0.8448144\tbest: 0.8547447 (21)\ttotal: 28s\tremaining: 6m 23s\n34:\ttest: 0.8452058\tbest: 0.8547447 (21)\ttotal: 28.6s\tremaining: 6m 19s\n35:\ttest: 0.8446299\tbest: 0.8547447 (21)\ttotal: 29.3s\tremaining: 6m 17s\n36:\ttest: 0.8441620\tbest: 0.8547447 (21)\ttotal: 30.2s\tremaining: 6m 17s\n37:\ttest: 0.8457593\tbest: 0.8547447 (21)\ttotal: 31.1s\tremaining: 6m 17s\n38:\ttest: 0.8453588\tbest: 0.8547447 (21)\ttotal: 32s\tremaining: 6m 17s\n39:\ttest: 0.8452868\tbest: 0.8547447 (21)\ttotal: 32.9s\tremaining: 6m 17s\n40:\ttest: 0.8449089\tbest: 0.8547447 (21)\ttotal: 33.6s\tremaining: 6m 15s\n41:\ttest: 0.8445849\tbest: 0.8547447 (21)\ttotal: 34.4s\tremaining: 6m 14s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8547446569\nbestIteration = 21\n\nShrink model to first 22 iterations.\n0:\ttest: 0.8036805\tbest: 0.8036805 (0)\ttotal: 1.1s\tremaining: 9m 9s\n1:\ttest: 0.8176265\tbest: 0.8176265 (1)\ttotal: 1.99s\tremaining: 8m 16s\n2:\ttest: 0.8074736\tbest: 0.8176265 (1)\ttotal: 2.7s\tremaining: 7m 26s\n3:\ttest: 0.8201800\tbest: 0.8201800 (3)\ttotal: 3.7s\tremaining: 7m 38s\n4:\ttest: 0.8372778\tbest: 0.8372778 (4)\ttotal: 4.6s\tremaining: 7m 35s\n5:\ttest: 0.8448954\tbest: 0.8448954 (5)\ttotal: 5.51s\tremaining: 7m 33s\n6:\ttest: 0.8446299\tbest: 0.8448954 (5)\ttotal: 6.51s\tremaining: 7m 38s\n7:\ttest: 0.8417953\tbest: 0.8448954 (5)\ttotal: 7.31s\tremaining: 7m 29s\n8:\ttest: 0.8509471\tbest: 0.8509471 (8)\ttotal: 8.21s\tremaining: 7m 27s\n9:\ttest: 0.8513071\tbest: 0.8513071 (9)\ttotal: 9.21s\tremaining: 7m 31s\n10:\ttest: 0.8498515\tbest: 0.8513071 (9)\ttotal: 10.3s\tremaining: 7m 38s\n11:\ttest: 0.8514938\tbest: 0.8514938 (11)\ttotal: 11.4s\tremaining: 7m 44s\n12:\ttest: 0.8532306\tbest: 0.8532306 (12)\ttotal: 12.3s\tremaining: 7m 41s\n13:\ttest: 0.8525219\tbest: 0.8532306 (12)\ttotal: 13.2s\tremaining: 7m 38s\n14:\ttest: 0.8543667\tbest: 0.8543667 (14)\ttotal: 13.9s\tremaining: 7m 29s\n15:\ttest: 0.8529719\tbest: 0.8543667 (14)\ttotal: 14.7s\tremaining: 7m 24s\n16:\ttest: 0.8522025\tbest: 0.8543667 (14)\ttotal: 15.7s\tremaining: 7m 26s\n17:\ttest: 0.8459258\tbest: 0.8543667 (14)\ttotal: 16.8s\tremaining: 7m 29s\n18:\ttest: 0.8426952\tbest: 0.8543667 (14)\ttotal: 17.7s\tremaining: 7m 28s\n19:\ttest: 0.8386277\tbest: 0.8543667 (14)\ttotal: 18.4s\tremaining: 7m 21s\n20:\ttest: 0.8361305\tbest: 0.8543667 (14)\ttotal: 19.5s\tremaining: 7m 24s\n21:\ttest: 0.8274421\tbest: 0.8543667 (14)\ttotal: 20.3s\tremaining: 7m 21s\n22:\ttest: 0.8256378\tbest: 0.8543667 (14)\ttotal: 21.3s\tremaining: 7m 21s\n23:\ttest: 0.8261192\tbest: 0.8543667 (14)\ttotal: 22.1s\tremaining: 7m 18s\n24:\ttest: 0.8278470\tbest: 0.8543667 (14)\ttotal: 23s\tremaining: 7m 17s\n25:\ttest: 0.8305872\tbest: 0.8543667 (14)\ttotal: 24s\tremaining: 7m 17s\n26:\ttest: 0.8303622\tbest: 0.8543667 (14)\ttotal: 24.9s\tremaining: 7m 16s\n27:\ttest: 0.8295388\tbest: 0.8543667 (14)\ttotal: 25.7s\tremaining: 7m 13s\n28:\ttest: 0.8309021\tbest: 0.8543667 (14)\ttotal: 26.7s\tremaining: 7m 13s\n29:\ttest: 0.8318155\tbest: 0.8543667 (14)\ttotal: 27.7s\tremaining: 7m 14s\n30:\ttest: 0.8321215\tbest: 0.8543667 (14)\ttotal: 28.2s\tremaining: 7m 6s\n31:\ttest: 0.8318740\tbest: 0.8543667 (14)\ttotal: 29.3s\tremaining: 7m 8s\n32:\ttest: 0.8283420\tbest: 0.8543667 (14)\ttotal: 30.2s\tremaining: 7m 7s\n33:\ttest: 0.8280225\tbest: 0.8543667 (14)\ttotal: 31.1s\tremaining: 7m 6s\n34:\ttest: 0.8262902\tbest: 0.8543667 (14)\ttotal: 32s\tremaining: 7m 5s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8543667042\nbestIteration = 14\n\nShrink model to first 15 iterations.\n0:\ttest: 0.6722835\tbest: 0.6722835 (0)\ttotal: 702ms\tremaining: 5m 50s\n1:\ttest: 0.7182520\tbest: 0.7182520 (1)\ttotal: 1.41s\tremaining: 5m 52s\n2:\ttest: 0.7210146\tbest: 0.7210146 (2)\ttotal: 2.2s\tremaining: 6m 4s\n3:\ttest: 0.7465107\tbest: 0.7465107 (3)\ttotal: 2.91s\tremaining: 6m\n4:\ttest: 0.7456085\tbest: 0.7465107 (3)\ttotal: 3.61s\tremaining: 5m 57s\n5:\ttest: 0.7488864\tbest: 0.7488864 (5)\ttotal: 4.5s\tremaining: 6m 10s\n6:\ttest: 0.7487064\tbest: 0.7488864 (5)\ttotal: 5.3s\tremaining: 6m 13s\n7:\ttest: 0.7470934\tbest: 0.7488864 (5)\ttotal: 6.1s\tremaining: 6m 15s\n8:\ttest: 0.7539820\tbest: 0.7539820 (8)\ttotal: 6.8s\tremaining: 6m 10s\n9:\ttest: 0.7519325\tbest: 0.7539820 (8)\ttotal: 7.39s\tremaining: 6m 2s\n10:\ttest: 0.7530191\tbest: 0.7539820 (8)\ttotal: 8.1s\tremaining: 6m\n11:\ttest: 0.7573521\tbest: 0.7573521 (11)\ttotal: 8.9s\tremaining: 6m 1s\n12:\ttest: 0.7581642\tbest: 0.7581642 (12)\ttotal: 9.8s\tremaining: 6m 7s\n13:\ttest: 0.7592171\tbest: 0.7592171 (13)\ttotal: 10.8s\tremaining: 6m 14s\n14:\ttest: 0.7617008\tbest: 0.7617008 (14)\ttotal: 11.7s\tremaining: 6m 18s\n15:\ttest: 0.7596625\tbest: 0.7617008 (14)\ttotal: 12.4s\tremaining: 6m 15s\n16:\ttest: 0.7585062\tbest: 0.7617008 (14)\ttotal: 13.2s\tremaining: 6m 14s\n17:\ttest: 0.7563150\tbest: 0.7617008 (14)\ttotal: 14.2s\tremaining: 6m 20s\n18:\ttest: 0.7582677\tbest: 0.7617008 (14)\ttotal: 15s\tremaining: 6m 19s\n19:\ttest: 0.7575568\tbest: 0.7617008 (14)\ttotal: 15.9s\tremaining: 6m 21s\n20:\ttest: 0.7606794\tbest: 0.7617008 (14)\ttotal: 16.8s\tremaining: 6m 23s\n21:\ttest: 0.7609944\tbest: 0.7617008 (14)\ttotal: 17.7s\tremaining: 6m 24s\n22:\ttest: 0.7619168\tbest: 0.7619168 (22)\ttotal: 18.3s\tremaining: 6m 19s\n23:\ttest: 0.7601485\tbest: 0.7619168 (22)\ttotal: 19.3s\tremaining: 6m 22s\n24:\ttest: 0.7602250\tbest: 0.7619168 (22)\ttotal: 20.2s\tremaining: 6m 23s\n25:\ttest: 0.7622227\tbest: 0.7622227 (25)\ttotal: 21s\tremaining: 6m 22s\n26:\ttest: 0.7633161\tbest: 0.7633161 (26)\ttotal: 21.9s\tremaining: 6m 23s\n27:\ttest: 0.7624252\tbest: 0.7633161 (26)\ttotal: 22.9s\tremaining: 6m 25s\n28:\ttest: 0.7628481\tbest: 0.7633161 (26)\ttotal: 23.6s\tremaining: 6m 23s\n29:\ttest: 0.7629156\tbest: 0.7633161 (26)\ttotal: 24.3s\tremaining: 6m 20s\n30:\ttest: 0.7618043\tbest: 0.7633161 (26)\ttotal: 25.1s\tremaining: 6m 19s\n31:\ttest: 0.7607199\tbest: 0.7633161 (26)\ttotal: 26.2s\tremaining: 6m 23s\n32:\ttest: 0.7598470\tbest: 0.7633161 (26)\ttotal: 27.1s\tremaining: 6m 23s\n33:\ttest: 0.7601305\tbest: 0.7633161 (26)\ttotal: 27.8s\tremaining: 6m 20s\n34:\ttest: 0.7597930\tbest: 0.7633161 (26)\ttotal: 28.6s\tremaining: 6m 19s\n35:\ttest: 0.7599775\tbest: 0.7633161 (26)\ttotal: 29.4s\tremaining: 6m 18s\n36:\ttest: 0.7621417\tbest: 0.7633161 (26)\ttotal: 30.3s\tremaining: 6m 18s\n37:\ttest: 0.7634061\tbest: 0.7634061 (37)\ttotal: 31s\tremaining: 6m 16s\n38:\ttest: 0.7646299\tbest: 0.7646299 (38)\ttotal: 31.9s\tremaining: 6m 16s\n39:\ttest: 0.7645309\tbest: 0.7646299 (38)\ttotal: 33s\tremaining: 6m 19s\n40:\ttest: 0.7645669\tbest: 0.7646299 (38)\ttotal: 33.8s\tremaining: 6m 18s\n41:\ttest: 0.7647019\tbest: 0.7647019 (41)\ttotal: 34.7s\tremaining: 6m 18s\n42:\ttest: 0.7656333\tbest: 0.7656333 (42)\ttotal: 35.6s\tremaining: 6m 18s\n43:\ttest: 0.7660112\tbest: 0.7660112 (43)\ttotal: 36.6s\tremaining: 6m 19s\n44:\ttest: 0.7664027\tbest: 0.7664027 (44)\ttotal: 37.4s\tremaining: 6m 18s\n45:\ttest: 0.7665602\tbest: 0.7665602 (45)\ttotal: 38.1s\tremaining: 6m 15s\n46:\ttest: 0.7670326\tbest: 0.7670326 (46)\ttotal: 38.9s\tremaining: 6m 14s\n47:\ttest: 0.7672936\tbest: 0.7672936 (47)\ttotal: 39.7s\tremaining: 6m 13s\n48:\ttest: 0.7679820\tbest: 0.7679820 (48)\ttotal: 40.5s\tremaining: 6m 12s\n49:\ttest: 0.7683240\tbest: 0.7683240 (49)\ttotal: 41.4s\tremaining: 6m 12s\n50:\ttest: 0.7670326\tbest: 0.7683240 (49)\ttotal: 42.3s\tremaining: 6m 12s\n51:\ttest: 0.7674511\tbest: 0.7683240 (49)\ttotal: 43.2s\tremaining: 6m 12s\n52:\ttest: 0.7678335\tbest: 0.7683240 (49)\ttotal: 44s\tremaining: 6m 10s\n53:\ttest: 0.7662857\tbest: 0.7683240 (49)\ttotal: 44.9s\tremaining: 6m 10s\n54:\ttest: 0.7664837\tbest: 0.7683240 (49)\ttotal: 45.7s\tremaining: 6m 9s\n55:\ttest: 0.7671046\tbest: 0.7683240 (49)\ttotal: 46.5s\tremaining: 6m 8s\n56:\ttest: 0.7670056\tbest: 0.7683240 (49)\ttotal: 47.4s\tremaining: 6m 8s\n57:\ttest: 0.7663757\tbest: 0.7683240 (49)\ttotal: 48.2s\tremaining: 6m 7s\n58:\ttest: 0.7657458\tbest: 0.7683240 (49)\ttotal: 49.3s\tremaining: 6m 8s\n59:\ttest: 0.7656153\tbest: 0.7683240 (49)\ttotal: 50s\tremaining: 6m 6s\n60:\ttest: 0.7653858\tbest: 0.7683240 (49)\ttotal: 50.7s\tremaining: 6m 4s\n61:\ttest: 0.7651204\tbest: 0.7683240 (49)\ttotal: 51.7s\tremaining: 6m 5s\n62:\ttest: 0.7651969\tbest: 0.7683240 (49)\ttotal: 52.3s\tremaining: 6m 2s\n63:\ttest: 0.7656873\tbest: 0.7683240 (49)\ttotal: 53.1s\tremaining: 6m 1s\n64:\ttest: 0.7659663\tbest: 0.7683240 (49)\ttotal: 53.9s\tremaining: 6m\n65:\ttest: 0.7657728\tbest: 0.7683240 (49)\ttotal: 54.7s\tremaining: 5m 59s\n66:\ttest: 0.7659843\tbest: 0.7683240 (49)\ttotal: 55.5s\tremaining: 5m 58s\n67:\ttest: 0.7655613\tbest: 0.7683240 (49)\ttotal: 56.1s\tremaining: 5m 56s\n68:\ttest: 0.7649809\tbest: 0.7683240 (49)\ttotal: 56.8s\tremaining: 5m 54s\n69:\ttest: 0.7645804\tbest: 0.7683240 (49)\ttotal: 57.6s\tremaining: 5m 53s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7683239595\nbestIteration = 49\n\nShrink model to first 50 iterations.\n0:\ttest: 0.6802745\tbest: 0.6802745 (0)\ttotal: 885ms\tremaining: 7m 21s\n1:\ttest: 0.7564949\tbest: 0.7564949 (1)\ttotal: 1.78s\tremaining: 7m 24s\n2:\ttest: 0.7813611\tbest: 0.7813611 (2)\ttotal: 2.49s\tremaining: 6m 52s\n3:\ttest: 0.7996535\tbest: 0.7996535 (3)\ttotal: 3.48s\tremaining: 7m 11s\n4:\ttest: 0.8063352\tbest: 0.8063352 (4)\ttotal: 4.38s\tremaining: 7m 13s\n5:\ttest: 0.8310349\tbest: 0.8310349 (5)\ttotal: 5.28s\tremaining: 7m 15s\n6:\ttest: 0.8449111\tbest: 0.8449111 (6)\ttotal: 6.19s\tremaining: 7m 15s\n7:\ttest: 0.8439123\tbest: 0.8449111 (6)\ttotal: 7.08s\tremaining: 7m 15s\n8:\ttest: 0.8459978\tbest: 0.8459978 (8)\ttotal: 8.18s\tremaining: 7m 26s\n9:\ttest: 0.8467717\tbest: 0.8467717 (9)\ttotal: 9.09s\tremaining: 7m 25s\n10:\ttest: 0.8476670\tbest: 0.8476670 (10)\ttotal: 10.1s\tremaining: 7m 28s\n11:\ttest: 0.8467447\tbest: 0.8476670 (10)\ttotal: 10.9s\tremaining: 7m 22s\n12:\ttest: 0.8547942\tbest: 0.8547942 (12)\ttotal: 12s\tremaining: 7m 28s\n13:\ttest: 0.8440450\tbest: 0.8547942 (12)\ttotal: 12.9s\tremaining: 7m 27s\n14:\ttest: 0.8433206\tbest: 0.8547942 (12)\ttotal: 13.7s\tremaining: 7m 22s\n15:\ttest: 0.8438110\tbest: 0.8547942 (12)\ttotal: 14.7s\tremaining: 7m 23s\n16:\ttest: 0.8444364\tbest: 0.8547942 (12)\ttotal: 15.6s\tremaining: 7m 22s\n17:\ttest: 0.8445309\tbest: 0.8547942 (12)\ttotal: 16.3s\tremaining: 7m 15s\n18:\ttest: 0.8427402\tbest: 0.8547942 (12)\ttotal: 17.4s\tremaining: 7m 19s\n19:\ttest: 0.8386007\tbest: 0.8547942 (12)\ttotal: 18.4s\tremaining: 7m 21s\n20:\ttest: 0.8384657\tbest: 0.8547942 (12)\ttotal: 19.4s\tremaining: 7m 22s\n21:\ttest: 0.8388886\tbest: 0.8547942 (12)\ttotal: 20.2s\tremaining: 7m 18s\n22:\ttest: 0.8394826\tbest: 0.8547942 (12)\ttotal: 21.3s\tremaining: 7m 21s\n23:\ttest: 0.8402790\tbest: 0.8547942 (12)\ttotal: 22.3s\tremaining: 7m 21s\n24:\ttest: 0.8415163\tbest: 0.8547942 (12)\ttotal: 23.3s\tremaining: 7m 22s\n25:\ttest: 0.8410934\tbest: 0.8547942 (12)\ttotal: 24.3s\tremaining: 7m 22s\n26:\ttest: 0.8413633\tbest: 0.8547942 (12)\ttotal: 25s\tremaining: 7m 17s\n27:\ttest: 0.8415793\tbest: 0.8547942 (12)\ttotal: 25.8s\tremaining: 7m 14s\n28:\ttest: 0.8405804\tbest: 0.8547942 (12)\ttotal: 26.9s\tremaining: 7m 16s\n29:\ttest: 0.8401035\tbest: 0.8547942 (12)\ttotal: 27.9s\tremaining: 7m 16s\n30:\ttest: 0.8397525\tbest: 0.8547942 (12)\ttotal: 29s\tremaining: 7m 18s\n31:\ttest: 0.8386052\tbest: 0.8547942 (12)\ttotal: 29.7s\tremaining: 7m 14s\n32:\ttest: 0.8372103\tbest: 0.8547942 (12)\ttotal: 30.7s\tremaining: 7m 14s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8547941507\nbestIteration = 12\n\nShrink model to first 13 iterations.\n0:\ttest: 0.8036805\tbest: 0.8036805 (0)\ttotal: 996ms\tremaining: 8m 16s\n1:\ttest: 0.8180675\tbest: 0.8180675 (1)\ttotal: 1.9s\tremaining: 7m 52s\n2:\ttest: 0.7863870\tbest: 0.8180675 (1)\ttotal: 2.7s\tremaining: 7m 27s\n3:\ttest: 0.7978920\tbest: 0.8180675 (1)\ttotal: 3.69s\tremaining: 7m 38s\n4:\ttest: 0.7977548\tbest: 0.8180675 (1)\ttotal: 4.8s\tremaining: 7m 55s\n5:\ttest: 0.8204792\tbest: 0.8204792 (5)\ttotal: 5.8s\tremaining: 7m 57s\n6:\ttest: 0.8182745\tbest: 0.8204792 (5)\ttotal: 6.59s\tremaining: 7m 44s\n7:\ttest: 0.8235703\tbest: 0.8235703 (7)\ttotal: 7.6s\tremaining: 7m 47s\n8:\ttest: 0.8270664\tbest: 0.8270664 (8)\ttotal: 8.4s\tremaining: 7m 38s\n9:\ttest: 0.8328234\tbest: 0.8328234 (9)\ttotal: 9.1s\tremaining: 7m 25s\n10:\ttest: 0.8328999\tbest: 0.8328999 (10)\ttotal: 10s\tremaining: 7m 24s\n11:\ttest: 0.8338403\tbest: 0.8338403 (11)\ttotal: 10.8s\tremaining: 7m 19s\n12:\ttest: 0.8303442\tbest: 0.8338403 (11)\ttotal: 11.6s\tremaining: 7m 14s\n13:\ttest: 0.8304162\tbest: 0.8338403 (11)\ttotal: 12.4s\tremaining: 7m 10s\n14:\ttest: 0.8338448\tbest: 0.8338448 (14)\ttotal: 13.1s\tremaining: 7m 3s\n15:\ttest: 0.8338853\tbest: 0.8338853 (15)\ttotal: 13.9s\tremaining: 7m\n16:\ttest: 0.8348841\tbest: 0.8348841 (16)\ttotal: 14.8s\tremaining: 7m\n17:\ttest: 0.8296153\tbest: 0.8348841 (16)\ttotal: 15.5s\tremaining: 6m 54s\n18:\ttest: 0.8260562\tbest: 0.8348841 (16)\ttotal: 16.5s\tremaining: 6m 57s\n19:\ttest: 0.8260652\tbest: 0.8348841 (16)\ttotal: 17.4s\tremaining: 6m 57s\n20:\ttest: 0.8188616\tbest: 0.8348841 (16)\ttotal: 18s\tremaining: 6m 50s\n21:\ttest: 0.8238650\tbest: 0.8348841 (16)\ttotal: 18.9s\tremaining: 6m 50s\n22:\ttest: 0.8252688\tbest: 0.8348841 (16)\ttotal: 19.8s\tremaining: 6m 50s\n23:\ttest: 0.8250214\tbest: 0.8348841 (16)\ttotal: 20.5s\tremaining: 6m 46s\n24:\ttest: 0.8261462\tbest: 0.8348841 (16)\ttotal: 21.6s\tremaining: 6m 50s\n25:\ttest: 0.8267177\tbest: 0.8348841 (16)\ttotal: 22.5s\tremaining: 6m 49s\n26:\ttest: 0.8279505\tbest: 0.8348841 (16)\ttotal: 23.4s\tremaining: 6m 49s\n27:\ttest: 0.8309246\tbest: 0.8348841 (16)\ttotal: 24.4s\tremaining: 6m 51s\n28:\ttest: 0.8310461\tbest: 0.8348841 (16)\ttotal: 25.3s\tremaining: 6m 50s\n29:\ttest: 0.8317390\tbest: 0.8348841 (16)\ttotal: 26.1s\tremaining: 6m 48s\n30:\ttest: 0.8304837\tbest: 0.8348841 (16)\ttotal: 27s\tremaining: 6m 48s\n31:\ttest: 0.8313701\tbest: 0.8348841 (16)\ttotal: 27.8s\tremaining: 6m 46s\n32:\ttest: 0.8330034\tbest: 0.8348841 (16)\ttotal: 28.7s\tremaining: 6m 45s\n33:\ttest: 0.8327514\tbest: 0.8348841 (16)\ttotal: 29.5s\tremaining: 6m 44s\n34:\ttest: 0.8325264\tbest: 0.8348841 (16)\ttotal: 30.5s\tremaining: 6m 45s\n35:\ttest: 0.8322070\tbest: 0.8348841 (16)\ttotal: 31.1s\tremaining: 6m 40s\n36:\ttest: 0.8316760\tbest: 0.8348841 (16)\ttotal: 31.9s\tremaining: 6m 39s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8348841395\nbestIteration = 16\n\nShrink model to first 17 iterations.\n0:\ttest: 0.6722835\tbest: 0.6722835 (0)\ttotal: 401ms\tremaining: 3m 20s\n1:\ttest: 0.7027807\tbest: 0.7027807 (1)\ttotal: 1.3s\tremaining: 5m 23s\n2:\ttest: 0.6973611\tbest: 0.7027807 (1)\ttotal: 2.2s\tremaining: 6m 5s\n3:\ttest: 0.7343172\tbest: 0.7343172 (3)\ttotal: 3.19s\tremaining: 6m 36s\n4:\ttest: 0.7482812\tbest: 0.7482812 (4)\ttotal: 4s\tremaining: 6m 35s\n5:\ttest: 0.7488639\tbest: 0.7488639 (5)\ttotal: 4.99s\tremaining: 6m 51s\n6:\ttest: 0.7528819\tbest: 0.7528819 (6)\ttotal: 5.8s\tremaining: 6m 48s\n7:\ttest: 0.7473363\tbest: 0.7528819 (6)\ttotal: 6.8s\tremaining: 6m 58s\n8:\ttest: 0.7494398\tbest: 0.7528819 (6)\ttotal: 7.5s\tremaining: 6m 49s\n9:\ttest: 0.7469111\tbest: 0.7528819 (6)\ttotal: 8.39s\tremaining: 6m 51s\n10:\ttest: 0.7472733\tbest: 0.7528819 (6)\ttotal: 9.3s\tremaining: 6m 53s\n11:\ttest: 0.7547087\tbest: 0.7547087 (11)\ttotal: 9.99s\tremaining: 6m 46s\n12:\ttest: 0.7568999\tbest: 0.7568999 (12)\ttotal: 11.1s\tremaining: 6m 55s\n13:\ttest: 0.7607064\tbest: 0.7607064 (13)\ttotal: 12s\tremaining: 6m 56s\n14:\ttest: 0.7617863\tbest: 0.7617863 (14)\ttotal: 12.9s\tremaining: 6m 56s\n15:\ttest: 0.7590191\tbest: 0.7617863 (14)\ttotal: 13.6s\tremaining: 6m 51s\n16:\ttest: 0.7582857\tbest: 0.7617863 (14)\ttotal: 14.6s\tremaining: 6m 54s\n17:\ttest: 0.7596175\tbest: 0.7617863 (14)\ttotal: 15.5s\tremaining: 6m 54s\n18:\ttest: 0.7622317\tbest: 0.7622317 (18)\ttotal: 16.3s\tremaining: 6m 52s\n19:\ttest: 0.7608954\tbest: 0.7622317 (18)\ttotal: 17.3s\tremaining: 6m 55s\n20:\ttest: 0.7646299\tbest: 0.7646299 (20)\ttotal: 18s\tremaining: 6m 50s\n21:\ttest: 0.7604814\tbest: 0.7646299 (20)\ttotal: 18.9s\tremaining: 6m 50s\n22:\ttest: 0.7604904\tbest: 0.7646299 (20)\ttotal: 19.6s\tremaining: 6m 46s\n23:\ttest: 0.7626142\tbest: 0.7646299 (20)\ttotal: 20.9s\tremaining: 6m 54s\n24:\ttest: 0.7619438\tbest: 0.7646299 (20)\ttotal: 21.8s\tremaining: 6m 54s\n25:\ttest: 0.7605849\tbest: 0.7646299 (20)\ttotal: 22.4s\tremaining: 6m 48s\n26:\ttest: 0.7590596\tbest: 0.7646299 (20)\ttotal: 23.4s\tremaining: 6m 49s\n27:\ttest: 0.7611069\tbest: 0.7646299 (20)\ttotal: 24.6s\tremaining: 6m 54s\n28:\ttest: 0.7592261\tbest: 0.7646299 (20)\ttotal: 25.4s\tremaining: 6m 52s\n29:\ttest: 0.7568999\tbest: 0.7646299 (20)\ttotal: 26.4s\tremaining: 6m 53s\n30:\ttest: 0.7556175\tbest: 0.7646299 (20)\ttotal: 27s\tremaining: 6m 48s\n31:\ttest: 0.7557345\tbest: 0.7646299 (20)\ttotal: 28.2s\tremaining: 6m 52s\n32:\ttest: 0.7549111\tbest: 0.7646299 (20)\ttotal: 29.1s\tremaining: 6m 51s\n33:\ttest: 0.7545422\tbest: 0.7646299 (20)\ttotal: 30.2s\tremaining: 6m 53s\n34:\ttest: 0.7544567\tbest: 0.7646299 (20)\ttotal: 31.2s\tremaining: 6m 54s\n35:\ttest: 0.7555051\tbest: 0.7646299 (20)\ttotal: 32.1s\tremaining: 6m 53s\n36:\ttest: 0.7543037\tbest: 0.7646299 (20)\ttotal: 33s\tremaining: 6m 52s\n37:\ttest: 0.7535973\tbest: 0.7646299 (20)\ttotal: 34.1s\tremaining: 6m 54s\n38:\ttest: 0.7533408\tbest: 0.7646299 (20)\ttotal: 34.8s\tremaining: 6m 51s\n39:\ttest: 0.7541552\tbest: 0.7646299 (20)\ttotal: 35.5s\tremaining: 6m 48s\n40:\ttest: 0.7525984\tbest: 0.7646299 (20)\ttotal: 36.6s\tremaining: 6m 49s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.7646299213\nbestIteration = 20\n\nShrink model to first 21 iterations.\n0:\ttest: 0.6802745\tbest: 0.6802745 (0)\ttotal: 791ms\tremaining: 6m 34s\n1:\ttest: 0.7921980\tbest: 0.7921980 (1)\ttotal: 1.7s\tremaining: 7m 2s\n2:\ttest: 0.8054871\tbest: 0.8054871 (2)\ttotal: 2.79s\tremaining: 7m 41s\n3:\ttest: 0.8110011\tbest: 0.8110011 (3)\ttotal: 3.78s\tremaining: 7m 49s\n4:\ttest: 0.8048414\tbest: 0.8110011 (3)\ttotal: 4.59s\tremaining: 7m 34s\n5:\ttest: 0.8096108\tbest: 0.8110011 (3)\ttotal: 5.49s\tremaining: 7m 31s\n6:\ttest: 0.8104702\tbest: 0.8110011 (3)\ttotal: 6.29s\tremaining: 7m 22s\n7:\ttest: 0.8169651\tbest: 0.8169651 (7)\ttotal: 7.19s\tremaining: 7m 22s\n8:\ttest: 0.8213813\tbest: 0.8213813 (8)\ttotal: 8.09s\tremaining: 7m 21s\n9:\ttest: 0.8205782\tbest: 0.8213813 (8)\ttotal: 8.99s\tremaining: 7m 20s\n10:\ttest: 0.8222407\tbest: 0.8222407 (10)\ttotal: 9.98s\tremaining: 7m 23s\n11:\ttest: 0.8229741\tbest: 0.8229741 (11)\ttotal: 11.2s\tremaining: 7m 34s\n12:\ttest: 0.8239910\tbest: 0.8239910 (12)\ttotal: 12s\tremaining: 7m 28s\n13:\ttest: 0.8183442\tbest: 0.8239910 (12)\ttotal: 12.9s\tremaining: 7m 27s\n14:\ttest: 0.8187447\tbest: 0.8239910 (12)\ttotal: 13.4s\tremaining: 7m 13s\n15:\ttest: 0.8174533\tbest: 0.8239910 (12)\ttotal: 14.4s\tremaining: 7m 14s\n16:\ttest: 0.8172823\tbest: 0.8239910 (12)\ttotal: 15.2s\tremaining: 7m 11s\n17:\ttest: 0.8176783\tbest: 0.8239910 (12)\ttotal: 16s\tremaining: 7m 7s\n18:\ttest: 0.8155816\tbest: 0.8239910 (12)\ttotal: 17.1s\tremaining: 7m 12s\n19:\ttest: 0.8149876\tbest: 0.8239910 (12)\ttotal: 17.7s\tremaining: 7m 4s\n20:\ttest: 0.8156355\tbest: 0.8239910 (12)\ttotal: 18.7s\tremaining: 7m 6s\n21:\ttest: 0.8183037\tbest: 0.8239910 (12)\ttotal: 19.5s\tremaining: 7m 3s\n22:\ttest: 0.8142047\tbest: 0.8239910 (12)\ttotal: 20.3s\tremaining: 7m\n23:\ttest: 0.8134623\tbest: 0.8239910 (12)\ttotal: 21.1s\tremaining: 6m 58s\n24:\ttest: 0.8136108\tbest: 0.8239910 (12)\ttotal: 21.9s\tremaining: 6m 55s\n25:\ttest: 0.8137098\tbest: 0.8239910 (12)\ttotal: 22.9s\tremaining: 6m 57s\n26:\ttest: 0.8143307\tbest: 0.8239910 (12)\ttotal: 23.6s\tremaining: 6m 53s\n27:\ttest: 0.8116355\tbest: 0.8239910 (12)\ttotal: 24.6s\tremaining: 6m 54s\n28:\ttest: 0.8107447\tbest: 0.8239910 (12)\ttotal: 25.5s\tremaining: 6m 53s\n29:\ttest: 0.8110416\tbest: 0.8239910 (12)\ttotal: 26.1s\tremaining: 6m 48s\n30:\ttest: 0.8107492\tbest: 0.8239910 (12)\ttotal: 26.9s\tremaining: 6m 46s\n31:\ttest: 0.8106817\tbest: 0.8239910 (12)\ttotal: 27.8s\tremaining: 6m 46s\n32:\ttest: 0.8127919\tbest: 0.8239910 (12)\ttotal: 28.5s\tremaining: 6m 43s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8239910011\nbestIteration = 12\n\nShrink model to first 13 iterations.\n0:\ttest: 0.8036805\tbest: 0.8036805 (0)\ttotal: 792ms\tremaining: 6m 35s\n1:\ttest: 0.8176940\tbest: 0.8176940 (1)\ttotal: 1.49s\tremaining: 6m 10s\n2:\ttest: 0.8253408\tbest: 0.8253408 (2)\ttotal: 2.1s\tremaining: 5m 47s\n3:\ttest: 0.8266659\tbest: 0.8266659 (3)\ttotal: 2.99s\tremaining: 6m 10s\n4:\ttest: 0.8191946\tbest: 0.8266659 (3)\ttotal: 3.89s\tremaining: 6m 25s\n5:\ttest: 0.8229764\tbest: 0.8266659 (3)\ttotal: 4.69s\tremaining: 6m 26s\n6:\ttest: 0.8108031\tbest: 0.8266659 (3)\ttotal: 5.59s\tremaining: 6m 33s\n7:\ttest: 0.8008211\tbest: 0.8266659 (3)\ttotal: 6.49s\tremaining: 6m 39s\n8:\ttest: 0.8252936\tbest: 0.8266659 (3)\ttotal: 7.39s\tremaining: 6m 43s\n9:\ttest: 0.8256063\tbest: 0.8266659 (3)\ttotal: 8s\tremaining: 6m 31s\n10:\ttest: 0.8249944\tbest: 0.8266659 (3)\ttotal: 8.99s\tremaining: 6m 39s\n11:\ttest: 0.8305152\tbest: 0.8305152 (11)\ttotal: 9.79s\tremaining: 6m 38s\n12:\ttest: 0.8258133\tbest: 0.8305152 (11)\ttotal: 10.8s\tremaining: 6m 44s\n13:\ttest: 0.8230371\tbest: 0.8305152 (11)\ttotal: 11.8s\tremaining: 6m 49s\n14:\ttest: 0.8239100\tbest: 0.8305152 (11)\ttotal: 12.5s\tremaining: 6m 43s\n15:\ttest: 0.8262587\tbest: 0.8305152 (11)\ttotal: 13.2s\tremaining: 6m 38s\n16:\ttest: 0.8287334\tbest: 0.8305152 (11)\ttotal: 14s\tremaining: 6m 37s\n17:\ttest: 0.8289899\tbest: 0.8305152 (11)\ttotal: 14.7s\tremaining: 6m 33s\n18:\ttest: 0.8306592\tbest: 0.8306592 (18)\ttotal: 15.7s\tremaining: 6m 37s\n19:\ttest: 0.8300967\tbest: 0.8306592 (18)\ttotal: 16.5s\tremaining: 6m 35s\n20:\ttest: 0.8298493\tbest: 0.8306592 (18)\ttotal: 17.4s\tremaining: 6m 36s\n21:\ttest: 0.8294263\tbest: 0.8306592 (18)\ttotal: 18.1s\tremaining: 6m 33s\n22:\ttest: 0.8293273\tbest: 0.8306592 (18)\ttotal: 18.9s\tremaining: 6m 31s\n23:\ttest: 0.8306097\tbest: 0.8306592 (18)\ttotal: 19.5s\tremaining: 6m 26s\n24:\ttest: 0.8309966\tbest: 0.8309966 (24)\ttotal: 20.3s\tremaining: 6m 25s\n25:\ttest: 0.8313881\tbest: 0.8313881 (25)\ttotal: 21.1s\tremaining: 6m 24s\n26:\ttest: 0.8334578\tbest: 0.8334578 (26)\ttotal: 21.8s\tremaining: 6m 21s\n27:\ttest: 0.8336153\tbest: 0.8336153 (27)\ttotal: 22.7s\tremaining: 6m 22s\n28:\ttest: 0.8327019\tbest: 0.8336153 (27)\ttotal: 23.4s\tremaining: 6m 19s\n29:\ttest: 0.8315501\tbest: 0.8336153 (27)\ttotal: 24s\tremaining: 6m 15s\n30:\ttest: 0.8316850\tbest: 0.8336153 (27)\ttotal: 25s\tremaining: 6m 17s\n31:\ttest: 0.8312441\tbest: 0.8336153 (27)\ttotal: 25.7s\tremaining: 6m 15s\n32:\ttest: 0.8320225\tbest: 0.8336153 (27)\ttotal: 26.4s\tremaining: 6m 13s\n33:\ttest: 0.8324724\tbest: 0.8336153 (27)\ttotal: 27.2s\tremaining: 6m 12s\n34:\ttest: 0.8310191\tbest: 0.8336153 (27)\ttotal: 27.6s\tremaining: 6m 6s\n35:\ttest: 0.8308526\tbest: 0.8336153 (27)\ttotal: 28.4s\tremaining: 6m 5s\n36:\ttest: 0.8315546\tbest: 0.8336153 (27)\ttotal: 28.9s\tremaining: 6m 1s\n37:\ttest: 0.8313026\tbest: 0.8336153 (27)\ttotal: 29.8s\tremaining: 6m 1s\n38:\ttest: 0.8298403\tbest: 0.8336153 (27)\ttotal: 30.5s\tremaining: 6m\n39:\ttest: 0.8298178\tbest: 0.8336153 (27)\ttotal: 31.1s\tremaining: 5m 57s\n40:\ttest: 0.8299618\tbest: 0.8336153 (27)\ttotal: 31.7s\tremaining: 5m 54s\n41:\ttest: 0.8287694\tbest: 0.8336153 (27)\ttotal: 32.4s\tremaining: 5m 53s\n42:\ttest: 0.8286974\tbest: 0.8336153 (27)\ttotal: 33s\tremaining: 5m 50s\n43:\ttest: 0.8289494\tbest: 0.8336153 (27)\ttotal: 33.7s\tremaining: 5m 49s\n44:\ttest: 0.8284229\tbest: 0.8336153 (27)\ttotal: 34.4s\tremaining: 5m 47s\n45:\ttest: 0.8262092\tbest: 0.8336153 (27)\ttotal: 35.3s\tremaining: 5m 48s\n46:\ttest: 0.8259663\tbest: 0.8336153 (27)\ttotal: 36.3s\tremaining: 5m 49s\n47:\ttest: 0.8260832\tbest: 0.8336153 (27)\ttotal: 37.1s\tremaining: 5m 49s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8336152981\nbestIteration = 27\n\nShrink model to first 28 iterations.\n0:\ttest: 0.6693048\tbest: 0.6693048 (0)\ttotal: 786ms\tremaining: 6m 32s\n1:\ttest: 0.7204432\tbest: 0.7204432 (1)\ttotal: 1.4s\tremaining: 5m 47s\n2:\ttest: 0.7292621\tbest: 0.7292621 (2)\ttotal: 2.3s\tremaining: 6m 20s\n3:\ttest: 0.7507852\tbest: 0.7507852 (3)\ttotal: 3.1s\tremaining: 6m 23s\n4:\ttest: 0.7569539\tbest: 0.7569539 (4)\ttotal: 3.99s\tremaining: 6m 35s\n5:\ttest: 0.7575906\tbest: 0.7575906 (5)\ttotal: 4.78s\tremaining: 6m 33s\n6:\ttest: 0.7556558\tbest: 0.7575906 (5)\ttotal: 5.58s\tremaining: 6m 33s\n7:\ttest: 0.7595883\tbest: 0.7595883 (7)\ttotal: 6.38s\tremaining: 6m 32s\n8:\ttest: 0.7595231\tbest: 0.7595883 (7)\ttotal: 7.18s\tremaining: 6m 31s\n9:\ttest: 0.7578223\tbest: 0.7595883 (7)\ttotal: 7.89s\tremaining: 6m 26s\n10:\ttest: 0.7602182\tbest: 0.7602182 (10)\ttotal: 8.69s\tremaining: 6m 26s\n11:\ttest: 0.7609899\tbest: 0.7609899 (11)\ttotal: 9.48s\tremaining: 6m 25s\n12:\ttest: 0.7624612\tbest: 0.7624612 (12)\ttotal: 10.4s\tremaining: 6m 29s\n13:\ttest: 0.7611159\tbest: 0.7624612 (12)\ttotal: 11.2s\tremaining: 6m 28s\n14:\ttest: 0.7622002\tbest: 0.7624612 (12)\ttotal: 12.1s\tremaining: 6m 30s\n15:\ttest: 0.7638605\tbest: 0.7638605 (15)\ttotal: 12.8s\tremaining: 6m 27s\n16:\ttest: 0.7644634\tbest: 0.7644634 (16)\ttotal: 13.6s\tremaining: 6m 26s\n17:\ttest: 0.7650754\tbest: 0.7650754 (17)\ttotal: 14.4s\tremaining: 6m 25s\n18:\ttest: 0.7662722\tbest: 0.7662722 (18)\ttotal: 15.2s\tremaining: 6m 24s\n19:\ttest: 0.7659843\tbest: 0.7662722 (18)\ttotal: 16s\tremaining: 6m 23s\n20:\ttest: 0.7658358\tbest: 0.7662722 (18)\ttotal: 16.8s\tremaining: 6m 22s\n21:\ttest: 0.7637885\tbest: 0.7662722 (18)\ttotal: 17.7s\tremaining: 6m 24s\n22:\ttest: 0.7652733\tbest: 0.7662722 (18)\ttotal: 18.4s\tremaining: 6m 21s\n23:\ttest: 0.7651069\tbest: 0.7662722 (18)\ttotal: 19s\tremaining: 6m 16s\n24:\ttest: 0.7641350\tbest: 0.7662722 (18)\ttotal: 19.9s\tremaining: 6m 17s\n25:\ttest: 0.7644589\tbest: 0.7662722 (18)\ttotal: 20.8s\tremaining: 6m 18s\n26:\ttest: 0.7640900\tbest: 0.7662722 (18)\ttotal: 21.5s\tremaining: 6m 16s\n27:\ttest: 0.7644544\tbest: 0.7662722 (18)\ttotal: 22.3s\tremaining: 6m 15s\n28:\ttest: 0.7652373\tbest: 0.7662722 (18)\ttotal: 23.1s\tremaining: 6m 14s\n29:\ttest: 0.7656918\tbest: 0.7662722 (18)\ttotal: 23.9s\tremaining: 6m 14s\n30:\ttest: 0.7650079\tbest: 0.7662722 (18)\ttotal: 24.6s\tremaining: 6m 11s\n31:\ttest: 0.7657953\tbest: 0.7662722 (18)\ttotal: 25.5s\tremaining: 6m 12s\n32:\ttest: 0.7647784\tbest: 0.7662722 (18)\ttotal: 26.2s\tremaining: 6m 10s\n33:\ttest: 0.7642250\tbest: 0.7662722 (18)\ttotal: 27.1s\tremaining: 6m 11s\n34:\ttest: 0.7635006\tbest: 0.7662722 (18)\ttotal: 28s\tremaining: 6m 11s\n35:\ttest: 0.7634106\tbest: 0.7662722 (18)\ttotal: 28.7s\tremaining: 6m 9s\n36:\ttest: 0.7635006\tbest: 0.7662722 (18)\ttotal: 29.3s\tremaining: 6m 6s\n37:\ttest: 0.7628121\tbest: 0.7662722 (18)\ttotal: 29.8s\tremaining: 6m 2s\n38:\ttest: 0.7637120\tbest: 0.7662722 (18)\ttotal: 30.6s\tremaining: 6m 1s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.766272216\nbestIteration = 18\n\nShrink model to first 19 iterations.\n0:\ttest: 0.6805804\tbest: 0.6805804 (0)\ttotal: 886ms\tremaining: 7m 21s\n1:\ttest: 0.7562542\tbest: 0.7562542 (1)\ttotal: 1.59s\tremaining: 6m 35s\n2:\ttest: 0.7644117\tbest: 0.7644117 (2)\ttotal: 2.39s\tremaining: 6m 36s\n3:\ttest: 0.7601687\tbest: 0.7644117 (2)\ttotal: 3.29s\tremaining: 6m 47s\n4:\ttest: 0.8375006\tbest: 0.8375006 (4)\ttotal: 3.99s\tremaining: 6m 35s\n5:\ttest: 0.8491429\tbest: 0.8491429 (5)\ttotal: 4.78s\tremaining: 6m 33s\n6:\ttest: 0.8480540\tbest: 0.8491429 (5)\ttotal: 5.59s\tremaining: 6m 34s\n7:\ttest: 0.8454128\tbest: 0.8491429 (5)\ttotal: 6.69s\tremaining: 6m 51s\n8:\ttest: 0.8455298\tbest: 0.8491429 (5)\ttotal: 7.59s\tremaining: 6m 54s\n9:\ttest: 0.8474263\tbest: 0.8491429 (5)\ttotal: 8.38s\tremaining: 6m 50s\n10:\ttest: 0.8454128\tbest: 0.8491429 (5)\ttotal: 9.09s\tremaining: 6m 44s\n11:\ttest: 0.8476265\tbest: 0.8491429 (5)\ttotal: 9.99s\tremaining: 6m 46s\n12:\ttest: 0.8488414\tbest: 0.8491429 (5)\ttotal: 10.7s\tremaining: 6m 40s\n13:\ttest: 0.8483870\tbest: 0.8491429 (5)\ttotal: 11.5s\tremaining: 6m 38s\n14:\ttest: 0.8483780\tbest: 0.8491429 (5)\ttotal: 12.2s\tremaining: 6m 33s\n15:\ttest: 0.8515996\tbest: 0.8515996 (15)\ttotal: 12.8s\tremaining: 6m 26s\n16:\ttest: 0.8534938\tbest: 0.8534938 (16)\ttotal: 13.6s\tremaining: 6m 25s\n17:\ttest: 0.8546907\tbest: 0.8546907 (17)\ttotal: 14.5s\tremaining: 6m 27s\n18:\ttest: 0.8541102\tbest: 0.8546907 (17)\ttotal: 15.3s\tremaining: 6m 27s\n19:\ttest: 0.8547402\tbest: 0.8547402 (19)\ttotal: 16.1s\tremaining: 6m 26s\n20:\ttest: 0.8556085\tbest: 0.8556085 (20)\ttotal: 17.1s\tremaining: 6m 29s\n21:\ttest: 0.8552036\tbest: 0.8556085 (20)\ttotal: 18s\tremaining: 6m 30s\n22:\ttest: 0.8552981\tbest: 0.8556085 (20)\ttotal: 18.7s\tremaining: 6m 27s\n23:\ttest: 0.8489809\tbest: 0.8556085 (20)\ttotal: 19.7s\tremaining: 6m 30s\n24:\ttest: 0.8492013\tbest: 0.8556085 (20)\ttotal: 20.6s\tremaining: 6m 30s\n25:\ttest: 0.8417548\tbest: 0.8556085 (20)\ttotal: 21.5s\tremaining: 6m 31s\n26:\ttest: 0.8410124\tbest: 0.8556085 (20)\ttotal: 22.4s\tremaining: 6m 32s\n27:\ttest: 0.8412643\tbest: 0.8556085 (20)\ttotal: 23s\tremaining: 6m 27s\n28:\ttest: 0.8353926\tbest: 0.8556085 (20)\ttotal: 23.6s\tremaining: 6m 22s\n29:\ttest: 0.8356760\tbest: 0.8556085 (20)\ttotal: 24.4s\tremaining: 6m 21s\n30:\ttest: 0.8372553\tbest: 0.8556085 (20)\ttotal: 25.1s\tremaining: 6m 19s\n31:\ttest: 0.8370889\tbest: 0.8556085 (20)\ttotal: 25.9s\tremaining: 6m 18s\n32:\ttest: 0.8368684\tbest: 0.8556085 (20)\ttotal: 26.6s\tremaining: 6m 16s\n33:\ttest: 0.8377953\tbest: 0.8556085 (20)\ttotal: 27.4s\tremaining: 6m 15s\n34:\ttest: 0.8379483\tbest: 0.8556085 (20)\ttotal: 28.1s\tremaining: 6m 13s\n35:\ttest: 0.8390506\tbest: 0.8556085 (20)\ttotal: 29s\tremaining: 6m 13s\n36:\ttest: 0.8412958\tbest: 0.8556085 (20)\ttotal: 29.9s\tremaining: 6m 13s\n37:\ttest: 0.8416873\tbest: 0.8556085 (20)\ttotal: 30.7s\tremaining: 6m 13s\n38:\ttest: 0.8417368\tbest: 0.8556085 (20)\ttotal: 31.1s\tremaining: 6m 7s\n39:\ttest: 0.8416243\tbest: 0.8556085 (20)\ttotal: 32s\tremaining: 6m 7s\n40:\ttest: 0.8415208\tbest: 0.8556085 (20)\ttotal: 32.9s\tremaining: 6m 8s\nStopped by overfitting detector  (20 iterations wait)\n\nbestTest = 0.8556085489\nbestIteration = 20\n\nShrink model to first 21 iterations.\n0:\ttest: 0.8021867\tbest: 0.8021867 (0)\ttotal: 1s\tremaining: 8m 19s\n1:\ttest: 0.8167222\tbest: 0.8167222 (1)\ttotal: 1.71s\tremaining: 7m 4s\n2:\ttest: 0.8077525\tbest: 0.8167222 (1)\ttotal: 2.6s\tremaining: 7m 10s\n3:\ttest: 0.8198875\tbest: 0.8198875 (3)\ttotal: 3.21s\tremaining: 6m 37s\n4:\ttest: 0.8312103\tbest: 0.8312103 (4)\ttotal: 4.2s\tremaining: 6m 56s\n5:\ttest: 0.8416310\tbest: 0.8416310 (5)\ttotal: 5.01s\tremaining: 6m 52s\n6:\ttest: 0.8414938\tbest: 0.8416310 (5)\ttotal: 6.1s\tremaining: 7m 9s\n7:\ttest: 0.8398088\tbest: 0.8416310 (5)\ttotal: 7.1s\tremaining: 7m 16s\n8:\ttest: 0.8469471\tbest: 0.8469471 (8)\ttotal: 8.01s\tremaining: 7m 16s\n9:\ttest: 0.8470394\tbest: 0.8470394 (9)\ttotal: 9.01s\tremaining: 7m 21s\n10:\ttest: 0.8474308\tbest: 0.8474308 (10)\ttotal: 9.82s\tremaining: 7m 16s\n11:\ttest: 0.8498223\tbest: 0.8498223 (11)\ttotal: 10.5s\tremaining: 7m 7s\n12:\ttest: 0.8514871\tbest: 0.8514871 (12)\ttotal: 11.4s\tremaining: 7m 7s\n13:\ttest: 0.8503082\tbest: 0.8514871 (12)\ttotal: 12.3s\tremaining: 7m 7s\n","name":"stdout"}],"source":"from sklearn.model_selection import GridSearchCV\n\nans = np.zeros([test.shape[0]])\nskf = StratifiedKFold(n_splits=10, shuffle=True, random_state=7410)\nfor j, (train_index, val_index) in enumerate(skf.split(train, label)):\n    params = {'learning_rate': [0.05,0.1,0.2],\n            'depth': [6,8,10],\n            'l2_leaf_reg': [1, 3, 5, 7, 9]}\n    model = CatBoostClassifier(iterations=500, \n                            border_count=254,\n                            one_hot_max_size=10,\n                            cat_features=categorical_features_indices,\n                            loss_function='Logloss',\n                            eval_metric='AUC',\n                            logging_level='Verbose',\n                            early_stopping_rounds=20,\n                            use_best_model=True,\n                            thread_count=-1,\n                            counter_calc_method='Full')\n    search_model = GridSearchCV(model, param_grid = params,  cv = 3)#scoring='roc_auc',\n\n    grid_search_result = search_model.fit(train.iloc[train_index],label.iloc[train_index],eval_set=(val_online, val_online_label),)#plot=True\n    ans += grid_search_result.predict_proba(test)[:,1]/10\n","execution_count":15},{"metadata":{"id":"D6649D1FF5E943B180C3942632DB3A68","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 720x1080 with 1 Axes>","text/html":"<img src=\"/gridfs/static_files/rt_upload/D6649D1FF5E943B180C3942632DB3A68/qi3frkmhfz.png\">"},"transient":{}}],"source":"import matplotlib.pyplot as plt \nfrom matplotlib import cm\n%matplotlib inline\nscore = pd.DataFrame()\nscore['fea_name'] = model.feature_names_\nscore['fea']=model.feature_importances_\nscore = score.sort_values(['fea'], ascending=False)\ntemp = pd.DataFrame()\ntemp = score[:60]\ncolor = cm.jet(temp['fea']/temp['fea'].max())\nplt.figure(figsize=(10, 15))\nplt.barh(temp['fea_name'],temp['fea'],height =0.8,color=color,alpha=0.8)\nplt.show()","execution_count":7},{"metadata":{"id":"9F65FF842B444BC3AA444500EE69B94D","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_sub = pd.DataFrame({'ID':data_test['ID'].astype(int),'hepatitis':ans.flatten()})","execution_count":9},{"metadata":{"id":"009934E5598A49949AC83A94F607ED3A","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"# ans_sub.to_csv('sub.csv',index=0)","execution_count":10},{"metadata":{"id":"CA021E65006D47E4810F86F5DB314595","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_sub.describe()","execution_count":11},{"metadata":{"id":"8BDCA607C1C743B788171D18303FDC31","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# DeepFM"},{"metadata":{"id":"86D4B0D9104A4AB1A110F5767F8D61DF","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"from itertools import chain\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Model, Sequential\nfrom tensorflow.keras import optimizers, layers, losses\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\n\nimport pandas as pd\nfrom sklearn.metrics import log_loss, roc_auc_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler\n\n# from deepctr.feature_column import  SparseFeat, DenseFeat, get_feature_names, build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns\n# from deepctr.feature_column import build_input_features, get_linear_logit, input_from_feature_columns\n# from deepctr.layers.core import PredictionLayer, DNN\n# from deepctr.layers.interaction import SENETLayer, BilinearInteraction\n# from deepctr.layers.utils import concat_func, add_func, combined_dnn_input\n\nfrom deepctr.feature_column import  SparseFeat, DenseFeat, get_feature_names, build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns\nfrom deepctr.layers.core import PredictionLayer, DNN\nfrom deepctr.layers.interaction import FM\nfrom deepctr.layers.utils import concat_func, add_func, combined_dnn_input\n","execution_count":12},{"metadata":{"id":"B18A9AEA232642E18DB51B3980D63276","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"# data_test = pd.read_csv('test_.csv')\n# test = data_test.drop(columns=['ID'])\n\n# data_train = pd.read_csv('train_.csv')\n# train = data_train.drop(columns=['ID','肝炎'])\n# label = data_train['肝炎']\n\ndata = train.append(test)\ndata = data.append(val_online)","execution_count":13},{"metadata":{"id":"359FE19B6F30420494A3FB7465E1528F","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"dense_features=['Revenue','Sports_activities','Weight','Height',\n                'Body_mass_index','Waist','Highest_blood_pressure',\n                'Minimum_blood_pressure','Good_Cholesterol','Bad_Cholesterol',\n                'Total_Cholesterol','Age',]\n                #'Drinking_target_enc',\n                # 'Family_hepatitis_target_enc',\n                # 'Diabetes_target_enc',\n                # 'Obesity_waistline_target_enc',\n                # 'Family_hypertension_target_enc',\n                # 'Blood_lipid_abnormality_target_enc',\n                # 'PVD_target_enc',\n                # 'Poor_vision_target_enc',\n                # 'Education_target_enc',\n                # 'ALF_target_enc',\n                # 'Unmarried_target_enc',\n                # 'Area_target_enc',\n                # 'Chronic_fatigue_target_enc',\n                # 'Source_of_care_target_enc',\n                # 'Hypertension_target_enc',\n                # 'Gender_target_enc',\n                # 'Family_diabetes_target_enc',\nsparse_features = list(set(data.columns.tolist()).difference(set(dense_features)))#\n\nfor feat in sparse_features:\n    lbe = LabelEncoder()\n    data[feat] = lbe.fit_transform(data[feat])\nmms = MinMaxScaler(feature_range=(0, 1))\ndata[dense_features] = mms.fit_transform(data[dense_features])\n\n# 2.count #unique features for each sparse field,and record dense feature field name\n\nfixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)\n                        for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)\n                        for feat in dense_features]\n\ndnn_feature_columns = fixlen_feature_columns\nlinear_feature_columns = fixlen_feature_columns\n\nfeature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)\n\n# 3.generate input deepfm_data for model\n# train, test = train_test_split(deepfm_data, test_size=0.2)\n\ndeepfm_train = data.head(train.shape[0])\ndeepfm_test_ = data.tail(test.shape[0]+val_online.shape[0])\ndeepfm_test = deepfm_test_.head(test.shape[0])\ndeepfm_val_online = deepfm_test_.tail(val_online.shape[0])\n\ndeepfm_train = {name:deepfm_train[name] for name in feature_names}\ndeepfm_test = {name:deepfm_test[name] for name in feature_names}\ndeepfm_val_online = {name:deepfm_val_online[name] for name in feature_names}\n\ndeepfm_train_ = deepfm_train\n","execution_count":14},{"metadata":{"id":"77567FAEE30B48D58DE018E69BF337D3","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"def multi_category_focal_loss2(gamma=2., alpha=.25):\n    \"\"\"\n    Usage:\n     model.compile(loss=[multi_category_focal_loss2(\n         alpha=0.35, gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n    \"\"\"\n    epsilon = 1.e-7\n    gamma = float(gamma)\n    alpha = tf.constant(alpha, dtype=tf.float32)\n\n    def multi_category_focal_loss2_fixed(y_true, y_pred):\n        y_true = tf.cast(y_true, tf.float32)\n        y_pred = tf.clip_by_value(y_pred, epsilon, 1. - epsilon)\n\n        alpha_t = y_true * alpha + \\\n            (tf.ones_like(y_true) - y_true) * (1 - alpha)\n        y_t = tf.multiply(y_true, y_pred) + tf.multiply(1 - y_true, 1 - y_pred)\n        ce = -tf.math.log(y_t)\n        weight = tf.pow(tf.subtract(1., y_t), gamma)\n        fl = tf.multiply(tf.multiply(weight, ce), alpha_t)\n        loss = tf.reduce_mean(fl)\n        return loss\n\n    return multi_category_focal_loss2_fixed","execution_count":15},{"metadata":{"id":"4767A2909C56411F8A3A924A1EE88840","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":" def M( linear_feature_columns, dnn_feature_columns, fm_group=[DEFAULT_GROUP_NAME], dnn_hidden_units=(128, 128),\n        l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0,\n        dnn_activation='elu', dnn_use_bn=False, task='binary'):\n    \n    features = build_input_features(\n        linear_feature_columns + dnn_feature_columns)\n\n    inputs_list = list(features.values())\n\n    linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',\n                                    l2_reg=l2_reg_linear)\n\n    group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,\n                                                                        seed, support_group=True)\n\n    fm_logit = add_func([FM()(concat_func(v, axis=1))\n                         for k, v in group_embedding_dict.items() if k in fm_group])\n\n    dnn_input = combined_dnn_input(list(chain.from_iterable(\n        group_embedding_dict.values())), dense_value_list)\n    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)\n    dnn_logit = tf.keras.layers.Dense(\n        1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(dnn_output)\n\n    final_logit = add_func([linear_logit, fm_logit, dnn_logit])\n    output = PredictionLayer(task)(final_logit)\n    model = Model(inputs=[features], outputs=[output])\n\n    model.compile(optimizer=optimizers.Adam(2.5e-4),\n                loss=losses.binary_crossentropy,# multi_category_focal_loss2(alpha=0.35, gamma=2)\n                metrics=['AUC'])\n    return model","execution_count":16},{"metadata":{"id":"AE08B57E874243139513B7609A121DF2","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":" checkpoint = ModelCheckpoint(\"epoch_{epoch:02d}.hdf5\", \n                                save_weights_only=True, \n                                monitor='val_loss', \n                                verbose=1,\n                                save_best_only=False, \n                                mode='auto', period=1)\nearlystop_callback = EarlyStopping(monitor='val_auc',\n                                    min_delta=0.00001,\n                                    patience=20,\n                                    verbose=1,\n                                    mode='max',\n                                    baseline=None,\n                                    restore_best_weights=True,)\nreduce_lr_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_auc',\n                                                            factor=0.1,\n                                                            patience=10,\n                                                            min_lr=0.0000001)","execution_count":17},{"metadata":{"id":"1E64ACD9928B4ADC8348AA24E984A7F5","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## 单折"},{"metadata":{"id":"EF2057F6F5F24EBB8A1582925B1B0BF7","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"model = M(linear_feature_columns=linear_feature_columns, \n        dnn_feature_columns=dnn_feature_columns, task='binary')\n# model.summary()\nmodel.fit(deepfm_train,\n            label,\n            # validation_split=0.3,\n            validation_data=(deepfm_val_online, val_online_label),\n            epochs=500,\n            batch_size=100,\n            callbacks=[earlystop_callback])\n\ninput_test = deepfm_test\nans_mtx = model.predict(input_test,\n                        batch_size=100)","execution_count":10},{"metadata":{"id":"8D3A68D6183447579E677BE4760F4D84","notebookId":"5f872688bfe3ac0015df720f","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"## 10折"},{"metadata":{"id":"89EC6DF440FF417882702FB147D07530","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"import gc\nans_ = np.zeros([test.shape[0]])\nskf = StratifiedKFold(n_splits=10, shuffle=True, random_state=7410)\nfor j, (train_index, val_index) in enumerate(skf.split(train, label)):\n    deepfm_train = {name:deepfm_train_[name].iloc[train_index] for name in feature_names}\n    model = M(linear_feature_columns=linear_feature_columns, \n        dnn_feature_columns=dnn_feature_columns, task='binary')\n    model.fit(deepfm_train,\n            label.iloc[train_index],\n            # validation_split=0.3,\n            validation_data=(deepfm_val_online, val_online_label),\n            epochs=500,\n            batch_size=100,\n            callbacks=[earlystop_callback])\n    ans_ += model.predict(deepfm_test).flatten()/10\n    del model\n    gc.collect()","execution_count":18},{"metadata":{"id":"D93DCD641E894C1682F1ACBA881FC8D0","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_sub_ = pd.DataFrame({'ID':data_test['ID'].astype(int),'hepatitis':ans_.flatten()})","execution_count":20},{"metadata":{"id":"2391D3B74BFF43BFBC769FF851D28561","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"# ans_sub_.to_csv('sub_.csv',index=0)","execution_count":null},{"metadata":{"id":"FC8EB225DF1F4F28A9BF55EA5A5980F4","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_sub_.describe()","execution_count":21},{"metadata":{"id":"973B53B89D8D44339262B34EC5C560EA","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"mdEditEnable":false},"cell_type":"markdown","source":"# avg"},{"metadata":{"id":"3490A67C078043A28929075CAA199034","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_end = pd.merge(ans_sub_,ans_sub,how='inner',on='ID')","execution_count":22},{"metadata":{"id":"6E1CC61ED2644881816C2F1FD1F5034F","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_end['hepatitis'] = (ans_end['hepatitis_x']+ans_end['hepatitis_y'])*0.5","execution_count":23},{"metadata":{"id":"4DF3F52204B545A081C6CF5F8C68AE49","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_end = ans_end.drop(columns=['hepatitis_x','hepatitis_y'])","execution_count":24},{"metadata":{"id":"7590707EAD6843EA9F39D697870D436C","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_end.to_csv('sub.csv',index=0)","execution_count":25},{"metadata":{"id":"B0A66C6F943C46AC8836520F0CF572EF","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"ans_end.describe()","execution_count":26},{"metadata":{"id":"2D003B87BF4D49AC9EAF033D8FC3BCD3","notebookId":"5f7f3339bfe3ac0015a2f629","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[],"source":"","execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python","nbconvert_exporter":"python","file_extension":".py","version":"3.5.2","pygments_lexer":"ipython3"}},"nbformat":4,"nbformat_minor":0}